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Lecture PowerPoint Presentation
Topic: Intuition vs. Analysis in Executive Decision-MakingSuggested Theory: Dual-Process Theory (James)Representative Example: Steve Jobs’ intuition in product development at Apple.
You will be assigned a topic by your instructor at the start of the class on Friday. Your assignment is to create a scholarly mini-lecture on the topic using current and relevant research and theory. Your lecture will be supported by a set of 10 slides (10 minute presentation – one minute per slide) on your assigned topic. You will need to cite two scholarly sources per slide. Your lecture should be designed to prepare a student to answer an exam question on the topic, by providing them with critical information, sources, and information about the topic. You will also have an eleventh slide with a potential, application/scenario-based exam question that must be answered using an essay response.
As a separate assignment, you will write a handout to go with the lecture that will accompany your lecture for your students. Please see the “lecture handout” assignment instructions and submission area.
The slides should cover the following information:
You will present your lecture to your team members and receive feedback. Your team will select one of the group members’ lectures to present on Sunday to the class (and you will inform your instructor whose lecture was selected).
Please see the attached “example” for organizing the slides. All of the course readings are also provided here to assist.
Cite the sources “in-text” on the slides, and then include each of those references in your accompanying handout, with full reference information. You do not have to include a “references” slide but your handout MUST include each in-text reference from your slides.
You can modify your topic to an industry, organization, or niche-based participant set of your choice.
Below is the PPT Outline I came up with for the lecture.
1. Slide 1 – Intuition vs Analysis in Executive Decision Making?
Agenda,
Slide 2 – What is Intuition vs Analysis in Executive Decision Making?
Slide 3 – History of intuition & Analysis/data-driven decision making.
Slide 4 – Intuition and analysis of advantages, disadvantages, and challengers
Slide 5 – Intuition in Product Development
Slide 6 – how analysis decision-making is driving in big companies.
Slide 7 – Story of product development (Apple in developing Gorilla Glass or any other story)
Slide 8 – Intuition and analysis in start-up companies
Slide 9 – Intuition and analysis in large firm corporate.
Slide 10 – Summarise the topic content and describe potential research.
Slide 11 – Comp Question? Handout Instruction
Please download the instructions handout for writing your lecture handout to accompany your slide deck.
You can use sources from the course readings. You should also supplant those sources with additional content from your researched articles and sources to align with your topic.
Submit your assignment according to the requirements of your residency instructor’s schedule.
Your grade will be based on your lecture’s alignment with these principles (which are reflected in the rubric):
Content: Does your lecture content provide doctoral-level, clear, and accurate information with current, relevant, and helpful sources that align well with your lecture PowerPoint and the presentation you make to your cohort members?
Clarity: How clear and easily read is your handout? Are statistics precise? Do you give details about research practices and findings? Have you attributed new information about a theory or topic to the source who found it through their research? Can someone take your PowerPoint and handout and understand the topic through a new lens?
Integration of Knowledge:Did you cite at least 20 sources of information (i.e., two for each of your slides) and incorporate the information from those sources into your lecture handout, providing your reader with details about those articles, the research they described, and their findings? Are your citations relevant, current, and accurate? (Fake citations will take this score to zero).
BADM 838: Instructions for Writing a Handout to Accompany a Lecture
Writing a lecture handout is similar to writing a short chapter in a textbook. Textbooks and
lectures uniquely provide information to colleagues and learners by amalgamating multiple
sources and references with synthetic commentary to allow information sharing. Similar to a
course research paper, a lecture handout has an aligned topic, requiring focus, precision, and
clarity to convey quality information. Your handout will be graded on the following principles:
Content: Does your lecture content provide doctoral-level, clear, and accurate
information with current, relevant, and helpful sources that align well with your lecture
PowerPoint and the presentation you make to your cohort members?
Clarity: How clear and easily read is your handout? Are statistics precise? Do you give
details about research practices and findings? Have you attributed new information about
a theory or topic to the source who found it through their research? Can someone take
your PowerPoint and handout and understand the topic through a new lens?
Integration of Knowledge: Did you cite at least 20 sources of information (i.e., two for
each of your slides) and incorporate the information from those sources into your lecture
handout, providing your reader with details about those articles, the research they
described, and their findings? Are your citations relevant, current, and accurate? (Fake
citations will take this score to zero).
Presentation/Grammar/Style: APA style guide requires that writers and presenters use
logical, clear, and specific formatting and styles to convey clear, unbiased, relevant, and
accurate information to readers and listeners. Using APA-level headings, a thesis
statement, topic sentences for paragraphs, transition sentences and paragraphs,
appropriate citation style (in-text and reference list), and solid grammar and spelling. The
use of the Merriam-Webster dictionary for all questions about hyphenation, proper nouns,
and spelling is part of APA.
Compliance with Assignment Requirements: Your lecture handout should be between
10 and 15 pages, double-spaced, and include 20 citations, with at least 12 within the last
5 years. A title page and reference page does not count in the page count.
How to create a solid lecture handout paper (using the sample slide deck idea as an example):
1. Use your PowerPoint Agenda (slide 1) as your organization method.
2. Write about a page for each slide, using the 2 unique references on each slide as your
base source information. (You can use references more than once in the paper, but if you
ensure you use each of the 2 unique sources/slides on the section the slide covers, you
will reach 20 sources easily).
3. Use headings and subheadings. Each item on the agenda should have at least one heading
and possibly more. For example, in the sample slide deck agenda, one of the topics is
this: “Examples of B-Corps.” Thus, for this lecture handout page, a set of headings could
be this:
Level 1: Examples of B-Corps
Content of an intro paragraph that might discuss how B-Corps are listed on the BLabs website and can be sorted and searched for using multiple filters. A
statement of what filter was used could ensue for this example. I used “United
States, Women, and Customers” as my filter in this case. While I found 11
matching companies, the three I discuss as examples are We. Flow, Boston
Common Asset Management LLC, and Karner Blue Capital, LLC.
Level 2: We.Flow
(content)
Level 2: Boston Common Asset Management LLC
(content)
Level 2: Karner Blue Capital, LLC
(content)
(Content) might include information about each organization, relevant analysis of
the industry or sectors they operate in concerning strategy, innovation, or
decision-making, and publicly accessible content about those organizations.
4. Another page of the lecture handout could be headed “Small Businesses as B-Corps”
(i.e., slide 7 from the example). Here, instead of looking at actual small businesses, the
lecture handout might start with a discussion of the definition of small business, some
unique factors about small businesses, and how small business owners must think and
make decisions strategically to avoid failure.
5. Each page will contain practical and scholarly information because the topic is practical,
and the audience comprises scholarly business people with practical and academic
interests.
6. A summary and conclusion is a requirement of any APA paper and should appear in any
writing you make – even in an email! Next steps, ideas for future research, thoughts, or
questions about how your topic is relevant today are appropriate for that paragraph or
section.
7. A reference list culminates the paper – and it should be APA formatted, in alphabetical
order, and with appropriate DOI URLs (or journal links if no DOI exists).
Achieving B-Corp Status as an Innovative Business Model in Finance Industry
Ezra Rajahkumar
University of the Cumberlands
2024 Spring – StrThnk, DecMk & Inn (BADM-838-M50) – Full Term
Dr. Michelle Preiksaitis
February 10, 2024
What is a certified B-Corp?
We live in a business world that is extremely demanding and uses a myriad of resources,
which leaves us with the question of whether we will be able to sustain this modus operandi ten,
twenty, fifty, or a hundred years from now. Businesses have the social and environmental
responsibility to lessen today’s impact arising from business processes in order to provide future
generations with the same opportunities. The finance industry generates revenue, but is it able to
uphold its obligation to demonstrate high-performance standards to protect society? This lecture
will delve into the B-Corp status and how achieving it is an innovative business model in the
finance industry.
A certified B-Corp is a for-profit corporation that is certified by B-Lab, a non-profit
organization that was founded in 2006 to award corporations if they meet high standards of
transparency, accountability, and sustainability in their performance with the intention of
creating more societal value for stakeholders. The B-Corp status has emerged as a means to
evaluate the social, environmental, and economic impact of companies (Diez-Busto et al., 2022).
Moreover, this certification could be achieved by obtaining a high standard of performance in
areas such as employee benefits, charitable giving, supply chain practices, and input materials.
By achieving a B-Impact Assessment score of 80 or above and passing B-Lab’s risk review, a
corporation demonstrates social commitment by changing its corporate governance structure to
be accountable to all stakeholders, not just shareholders, and exhibit transparency by allowing
information about their performance measured against B-Lab’s standards to be publicly available
on their B-Corp profile on B-Lab’s website so that sustainability can be anchored (Carvalho et
al., 2022). The number of certified B-Corps increased significantly, and there are now more than
4000 B-Corps..
History of the B-Corp and B-Lap initiatives
B-Lab was founded in 2006 with the intention of creating a new corporation type that
would find a balance between purpose and profit. Moreover, B-Lab was seeking to take
advantage of the power of businesses in order to address social and environmental issues. Shortly
after, B-Lab created the B-Corp certification in order to challenge companies to become more
responsible and provide road maps for the future by making changes in the present (Villela et al.,
2021). Typically, the financial performance of corporations takes the spotlight, and other
potential issues are put on the back burner. However, the creation of the B-Corp certification
highlights the importance of social and environmental performance so that stakeholders such as
employees, clients, suppliers, and societies receive more care..
Companies such as Patagonia, the American retailer of outdoor recreation clothing, and
Ben & Jerry’s, the American ice cream, frozen yogurt, and sorbet manufacturer, became certified
B-Corps to emphasize their commitment to the environment and society. Furthermore, these and
the many companies that followed suit demonstrated that it was indeed realizable to make profits
without neglecting the social and environmental responsibilities.
This movement increased awareness as B-Lab and B-Corps have witnessed continuous
growth and rising influence, so corporate responsibility has become more than a business
byproduct (Kim & Schifeling, 2022). The B-Corp movement has, therefore, not only caused
accountability, transparency, and sustainability to become key pillars of business for many
corporations but has led to a global change as companies of different geographic areas have
sought to get certified, which further emphasizes that many companies around the world are
willing to perform their business practices in a responsible manner that could positively
influence communities.
B-Corp advantages and disadvantages
Many reputable foundations, as well as corporations, have shown support for B-Lab and
the B-Corp certification. Examples of supporters are the Ford Foundation, whose mission is to
reduce poverty and injustice around the world by promoting and advancing human achievement.
The Kendeda Fund, which is a private philanthropic foundation, and Prudential, one of the
largest carriers for life insurance and mutual funds, have also expressed their support. Working
with B-Lab or getting B-Corp certified is favorable. What are the advantages of becoming a BCorporation?
Companies in the finance industry that undergo the process of getting a B-Corp
certification are able to build trustworthy connections with their stakeholders so that long-term
relationships can be formed. Furthermore, employees can be attracted and kept through the
social-environmental practices carried out by the B-Corps (Cláudia & Froehlich, 2022). Lastly,
investors that share the same vision of B-Corps can back the mission to be more socially and
environmentally responsible, which, in turn, could enhance their reputation. However, becoming
a certified B-Corp also has drawbacks. Obtaining a B-Corp certification could be an exhausting
process as it could take months and, in some cases, years, which is not necessarily an incentive
for every corporation to undergo this transformation. Additionally, there are additional costs
associated with being a B-Corporation as specific criteria, such as ensuring that employees
receive a living wage, could be problematic for companies, especially when the profits are barely
enough for small and medium-sized companies to stay afloat. Incurring higher costs could also
decrease profits materially, so shareholders, especially in the finance industry, may be reluctant
to go this route (Paeleman et al., 2024). Therefore, companies in the finance industry must
evaluate whether they have the capacity to become a B-Corp. To B or not to B?
Examples of B-Corps
A myriad of companies has jumped on the “B-Train”. Getting a B-Corp certification
shows a company’s capacity to respond to society’s challenges and its intention to maintain a
balance between social, environmental, and economic logic (Tabares, 2021). As companies in
the finance industry are growing more mindful of scarce resources and adopting a more futureoriented approach, the main motivation behind being a B-Corp is an underlining element that the
company is upholding social and environmental standards (Alam et al., 2022). Some of the
finance companies that are B-Corps and their respective overall B-Impact score are:
1. Beneficial State Bank: Community development bank based in California prioritizing
environmental sustainability in banking practices. (158.9).
2. Amalgamated Bank – U.S. bank supporting sustainable organizations and social justice. (155.3).
3. Trillium Asset Management – Investment company from Boston offering services that advance
humankind towards a global sustainable economy. (140.6)
4. Lendwithcare – Platform facilitating lending to entrepreneurs in developing countries. (134.4)
5. RSF Social Finance: Financial services organization in San Francisco offering investing and
giving options to connect social entrepreneurs with capital. (131.5)
6. Triodos Bank – European bank focusing on sustainable and ethical banking practices. (131.3)
7. Aspiration – Financial firm in Los Angeles offering green banking to fight climate change. (128).
8. Ecology Building Society – UK mortgage company building a greener society through sustainable
residential and commercial mortgages funded by ethical savings accounts. (123.7)
9. MicroVest – U.S. investment firm providing capital to responsible financial institutions. (103.2)
10. Caprock: Provides customized wealth service in Idaho with minor environmental footprint. (93)
Daniel Lubetzky and the KIND story
Daniel Lubetzky is the founder of the famous snack food company KIND, which is
committed to providing healthy snacks with transparent ingredients. The KIND story began
when Lubetzky tried to create a snack that was nutritious as well as tasty due to the fact that he
grew up in a household that was conscious about health. He created the KIND company, which
became a trailblazer in the snack industry by offering products made with whole nuts, fruits, and
spices without using artificial flavors or preservatives. The company also prioritized social
responsibility, advocating for transparency in the labeling of snacks and supporting various
social causes. In 2010, KIND became a certified B-Corp, which stresses Lubetzky’s and KIND’s
commitment to bringing healthy food to the clients and operating in a manner that is socially and
environmentally responsible. Lubetzky turned a dream into destiny with a company where good
is created to make the world better. Lubetzky used the same guiding principle when he launched
the Starts With Us movement in 2021. In joint efforts with actor Jason Alexander (Pretty
Woman), businessman Mark Cuban (Dallas Mavericks), and politician Andrew Yang (Forward
Party), Lubetzky created the movement to overcome political and cultural division in America by
practicing curiosity, compassion, and courage to positively influence communities.
KIND is an example of how social and business objectives can be achieved
simultaneously by bringing about a social transformation that creates value (Mele et al., 2020). A
positive impact on society can be achieved while also having commercial success when there is
social purpose integrated into the business model, which can lead to innovation that fosters
activities with ethical, sustainable, or moral objectives (Moroz & Gamble, 2021).
Relevance for Finance Industry
The story of Daniel Lubetzky and KIND demonstrates the viability of amalgamating
business goals and social objectives. In the finance industry, achieving B-Corp status can be an
innovative business model as it can set apart a business from competitors by emphasizing
fundamental values that can be appealing to society.
Getting a B-Corp certification is a distinct method to differentiate oneself from other
finance companies by being in a unique circle with a title that is getting more and more
recognition and public appreciation. This differentiation also relays to stakeholders that the
company’s focal point is not only financial growth but also creating positive value for the
environment as well as society. This approach also spotlights that the company’s core values are
congruent with social and environmental expectations which demonstrates corporate
responsibility. Such sustainability-based management and corporate environmental engagement
accentuate that a company understands, cares about, and proactively works on economic
consequences, which in itself could lead to innovative performances (Wang & Zhang, 2023).
Exercising corporate social responsibility, which looks at new ways to protect society and care
for the environment, could also attract investors that share the same credos and evoke customer
loyalty that finds alignment between their own values and the values of the company. From a
financial standpoint, driving innovation despite financial constraints is an essential trait (Wang &
Zhang, 2023). Social, environmental, and governance activities could also influence
technological and non‐technological innovations, resulting in new products and services (Yousfi,
2024). Becoming a B-Corp in the finance industry can meet the ever-changing expectations of
society, and doing so could open up alternative paths that can achieve sustainable innovation.
Green Finance
Focusing on environmentally conscious finance has evolved into a new idea to promote
the sustainable development of public, private, and not-for-profit sectors: Green Finance.
Green finance is not solely a trend witnessed around the world, but it has grown into a paramount
aspect of corporations to achieve sustainable growth. Empirical research on the influence of
green finance on corporate environmental responsibility performance of nineteen Chinese
companies of varying industries such as manufacturing, mining, and electricity, considered
heavy polluters, shows that 75% of these companies fail to meet environmental obligations such
as reducing pollution (He et al., 2022). The result indicates that adopting advanced technologies
comes with the responsibility of responding to environmental challenges, which many companies
cannot uphold.
Green finance puts corporations in an active role in establishing and continuously
developing distinctive qualities such as awareness of risks, rationality in innovation, and
responsibility toward society (Zhang et al., 2022). Companies in the finance industry could
actively contribute to forward-looking services by offering green financial services that include
evoking paradigm shifts in corporate structures to develop responsible practices, providing
societal opportunities, implementing innovations, and building social networks that embrace a
green culture (Zhang et al., 2022).
Summary
As society demands a more responsible and sustainable approach to business practices,
corporations must re-evaluate their performances (Kirst et al., 2021). Embedding social
responsibility in a company’s DNA by combining its mission of growing financially with an
attitude of being conducive to improved sustainability performance is a game-changer that
should not be brushed aside (Mion et al., 2023). Sustainable goals, such as fighting poverty,
hunger, and inequalities, as well as using business practices to support quality education and the
responsible production of services in the finance industry, are needed to create a lasting change
aimed at addressing the most pressing problems of humanity. Corporations are vital players in
the achievement of these goals through the way they conduct business and must commit
themselves to eliminating unsustainable habits (Tabares, 2021).
What started as a vision became a much-needed global movement when B-Lab Corps
created the B-Corp certification, increasing the awareness of transparency, accountability, and
sustainability. Daniel Lubetzky’s KIND company and many other large corporations became
certified B-Corps, strengthening their desire to spearhead sustainability ambitions. Green finance
is an ideology derived from the demand for environmentally friendly business activities and
poses a form of innovation when purpose and profits are combined goals, leading to new services
and novel environmental solutions (Tabares, 2021). Achieving B-Corp status as an innovative
business model in the finance industry is crucial if future generations of companies are to receive
the same or better opportunities as today’s corporations. More importantly, the environment and
society of tomorrow can also be protected when companies use sustainable business practices.
Potential Research Topic, Method, Design, Participants, and Research Question
A potential research topic could be as follows:
Topic, Method, Design: A Qualitative Case-Study on how Finance Companies in Latin America
can Innovatively Contribute to Social Development by Obtaining a B-Corp Certification.
Participant(s): 400 finance companies in Latin America that have or will earn a B-Corp
certification from B-Lab.
Research Question: What specific actions could finance companies that have or will earn a BCorp certification from B-Lab perform to contribute to social development in their respective
countries innovatively?
Comp Exam Question:
B-Corps balance their social-environmental missions along with financial goals to trigger
social and environmental development (Gamble et al., 2020). A Chilean qualitative study of 425
companies in Latin America and the Caribbean revealed that the COVID-19 global pandemic
and economic situation have advanced the adoption of the B-Corp business model,
demonstrating a stronger focus on social welfare, economic growth, and preservation of natural
resources (Acevedo et al., 2024). Corporations could see a country as a brand, and by
incorporating sustainable business practices into their processes, the companies could create or
add social value while meeting their economic objectives (Cantele et al., 2023). What specific
actions could finance companies that have or will earn a B-Corp certification from B-Lab
perform to contribute to social development in their respective countries innovatively?
References
Acevedo-Duque, Á., Álvarez-Herranz, A. P., & Artigas, W. (2024). In search of the benefits in
certified B-Corporations. Revista de Ciencias Administrativas y Económicas. 13(26). 253271. https://doi.org/10.17163/ret.n26.2023.05
Alam, J., Boamah, M., MacMullen, D., Kochar, N., & Barrington, R. (2022). In search of the
benefits in certified B-Corporations. Canadian Journal of Nonprofit & Social Economy
Research/Revue canadienne de recherche sur les OSBL et l’économie sociale. 13. 96-105.
https://doi.org/10.29173/cjnser561
Cantele, S., Leardini, C., & Piubello-Orsini, L. (2023). Impactful B-Corps: A configurational
approach of organizational factors leading to high sustainability performance. Corporate
Social Responsibility & Environmental Management. 30(3). 1104-1120.
https://doi.org/10.1002/csr.2407
Carvalho, B., Arnim, W., & Ness, B. (2022). Can B-Corp certification anchor sustainability in
small and medium enterprises? Corporate Social Responsibility & Environmental
Management. 29(1). 293-304. https://doi.org/10.1002/csr.2192
Cláudia, W., & Froehlich, C. (2022). The B-Corp movement, advantages, and challenges: the
perception of certified Brazilian companies. Brazilian Journal of Management/Revista de
Administração da UFSM. 15(4). 596-614. http://dx.doi.org/10.5902/1983465969844
Diez-Busto, E., Sanchez-Ruiz, L., & Fernández-Laviada, A. (2022). B Corp certification: Why?
How? And what for? Journal of Cleaner Production. 372.
https://doi.org/10.1016/j.jclepro.2022.133801
Gamble, E., Simon, P., & Moroz, P. (2020). Measuring the integration of social and
environmental missions in hybrid organizations. Journal of Business Ethics. 167(2). 271284. https://doi.org/10.1007/s10551-019-04146-3
He, L., Zhong, T., & Gan, S. (2022). Green finance and corporate environmental responsibility:
evidence from heavily polluting listed enterprises in China. Environmental science and
pollution research. 29(49). 74081-74096. https://doi.org/10.1007/s11356-022-21065-5
Kim, S., &, Schifeling, T. (2022). B-Corp certification and its impact on organizations over time.
Administrative Science Quarterly, 67(3). 674-720.
https://doi.org/10.1177/00018392221091734
Kirst, R. W., Borchardt, M., De Carvalho, M. N. M., & Pereira, G. M. (2021). Best of the world
or better for the world? A systematic literature review on benefit corporations and certified
B-Corporations contribution to sustainable development. Corporate Social Responsibility
& Environmental Management. 28(6). 1822-1839. https://doi.org/10.1002/csr.2160
Mele, C., Russo-Spena, T., Pels, J., & Tregua, M. (2020). Social business innovation: A fresh
conceptualization of collective practices. Social Business. 10(1). 5-34.
http://dx.doi.org/10.1362/204440820X15813359568246
Moroz, P., & Gamble, E. N. (2021). Business model innovation as a window into adaptive
tensions: five paths on the B-Corp journey. Journal of Business Research. 125. 672-683.
http://dx.doi.org/10.1016/j.jbusres.2020.01.046
Mion, G., Loza-Adaui, C., Bonfanti, A., & De Crescenzo, V. (2023). Mission statements and
financial and sustainability performance: An exploratory study of benefit corporations
certified as B-Corps. Journal of Business Research, 157. Article 113585
http://dx.doi.org/10.1016/j.jbusres.2022.113585
Paeleman, I., Guenster, N., & Varnacker, T. (2024). The consequences of financial leverage:
certified B-Corporations’ advantages compared to common commercial firms. Journal of
Business Ethics, 189(3). 507-523. http://dx.doi.org/10.1007/s10551-023-05349-5
Tabares, S. (2021). Certified B-Corporations: an approach to tensions of sustainable-driven
hybrid business models in an emerging economy. Journal of cleaner production. 317.
http://dx.doi.org/10.1016/j.jclepro.2021.128380
Tabares, S. (2021). Do hybrid organizations contribute to sustainable development goals?
Evidence from B-Corps in Colombia. Journal of cleaner production. 280.
http://dx.doi.org/10.1016/j.jclepro.2020.124615
Villela, M., Bulgacov, S., & Morgan, G. (2021). B-Corp certification and its impact on
organizations over time. Journal of Business Ethics, 170(2). 343-357.
https://doi.org/10.1007/s10551-019-04372-9
Wang, Z., & Zhang, J. (2023). Nexus between corporate environmental performance and
corporate environmental responsibility on innovation performance. Environment,
Development & Sustainability. 25(10). 11645-11672.
https://doi.org/10.1007/s10668-022-02546-6
Yousfi, O. (2024). Does corporate social responsibility increase innovation? Evidence from
France. Creativity and Innovation Management.
https://doi.org/http://dx.doi.org/10.1111/caim.12586
Zhang, W., Liu, X., Liu, J., & Zhou, Y. (2022). Endogenous development of green finance and
cultivation mechanism of green bankers. Environmental science and pollution research.
29(11). 15816-15826. https://doi.org/10.1007/s11356-021-16933-5
6/14/2024
Agenda:
Achieving B-Corp Status as an innovative business model in Finance Industry
Achieving B-Corp Status
as an Innovative Business
Model in Finance Industry
•
•
•
•
•
•
•
•
•
•
•
Ezra Rajahkumar
PhD, University of the Cumberlands
BADM-838-M50, StrThnk, DecMk & Inn
Dr. Michelle Preiksaitis
February 10, 2024
1
2
What is a certified B-Corp?
History of the B-Corp and B-Lap initiatives
• B-Lab founded in 2006 with intention of creating a new
corporation type finding balance between purpose and profit
• B-Lab created the B-Corp certification with goal of challenging
companies to become more responsible and provide road maps
for the future by making changes in the present (Villela et al.,
2021)
• B-Lab and B-Corps have witnessed continuous growth and rising
influence so that corporate responsibility became more than a
business byproduct (Kim & Schifeling, 2022).
• B-Corp status is means to evaluate the social, environmental, and
economic impact of companies (Diez-Busto et al., 2022)
• Business meets high standards of verified performance, accountability,
and transparency on various factors (employee benefits, charitable
giving, supply chain practices, input materials, etc.)
• Demonstrates high social and environmental performance achieving B
Impact Assessment score of 80 or above
• Changes corporate governance structure with more accountability &
legal commitment
• Exhibits transparency and allows performance information to be publicly
available on B-Corp profile on B-Lab’s website (Carvalho et al., 2022)
3
Slide 1: What is a certified B-Corp?
Slide 2: History of the B-Corp and B-Lap initiatives
Slide 3: B-Corp advantages and disadvantages
Slide 4: Examples of B-Corps
Slide 5: Daniel Lubetzky and the KIND story
Slide 6: Relevance for Finance Industry
Slide 7: Green Finance
Slide 8: Summary
Slide 9: Conclusion
Slide 10: Potential Research Topic
Slide 11: References
4
6/14/2024
Examples of B-Corps
B-Corp advantages and disadvantages
•
• Advantages:
– Build trustworthy long-term relationships with stakeholders
– Attract employees and investors through social-environmental
practices when values are shared (Cláudia & Froehlich, 2022)
•
1. Beneficial State Bank:
Community development bank based in California prioritizing environmental
sustainability in banking practices. (158.9)
2. Amalgamated Bank:
U.S. bank supporting sustainable organizations and social justice. (155.3)
3. Trillium Asset Management:
Investment company from Boston offering services that advance humankind
towards a global sustainable economy. (140.6)
4. Lendwithcare: Platform facilitating lending to entrepreneurs in developing
countries. (134.4)
5. RSF Social Finance: Financial services organization in San Francisco offering
investing and giving options to connect social entrepreneurs with capital. (131.5)
• Disadvantages:
– Obtaining a B-Corp certification could be exhausting process and
could take months, even years
– Additional costs associated with being a B-Corporation for meeting
specific criteria (Paeleman et al., 2024)
• To B or Not to B?
5
7
Demonstrate capacity to respond to society’s challenges and intention to
maintain a balance between social, environmental, and economic logics (Tabares,
2021)
B-Corp is an underlining element that the company is upholding social and
environmental standards (Alam et al., 2022)
6
Daniel Lubetzky and the KIND story
Relevance for Finance Industry
• Daniel Lubetzky is founder of famous snack food company KIND which is
committed to providing healthy snacks with transparent ingredients
• Prioritized social responsibility, advocating for transparency in labeling of
snacks, and supporting various social causes
• In 2010, KIND became a certified B-Corp for operating socially responsibly
• KIND is example of how social and business objectives can be achieved
simultaneously by bringing about a social transformation that creates
value (Mele et al., 2020)
• Business model which can lead to innovation fostering activities with
ethical, sustainable, or moral objectives (Moroz & Gamble, 2021).
• Lubetzky created Starts With Us movement in 2021 with actor Jason
Alexander (Pretty Woman), businessman Mark Cuban (Dallas Mavericks),
and politician Andrew Yang (Forward Party) to overcome political and
cultural division in America by practicing curiosity, compassion, and
courage to positively influence communities.
• In finance, achieving B-Corp status can be an innovative business
model and set apart a business from competitors by emphasizing
key values that appeal to society.
• Sustainability-based management and corporate environmental
engagement accentuates that a company understands, cares
about, and proactively works on economic consequences which in
itself could lead to innovative performances (Wang & Zhang, 2023)
• From finance standpoint, driving innovation despite financial
constraints is an essential trait
• Social, environmental, and governance activities could also
influence technological and non-technological innovations
resulting in new products and services (Yousfi, 2024)
8
6/14/2024
Green Finance
Summary
• Environmentally conscious finance to achieve sustainable growth
• Empirical research on the influence of green finance on corporate
environmental responsibility performance:
– Nineteen Chinese companies of varying industries such as
manufacturing, mining, and, electricity considered heavy
polluters
– 75% of these companies fail to meet environmental obligations
such as reducing pollution (He et al., 2022).
– Result indicates: Adopting advanced technologies comes with
responsibility of responding to environmental challenges
– Many companies cannot meet expectations
• Green finance puts corporations in active role to establish and
continuously develop distinctive qualities: awareness of risks,
rationality in innovation, responsibility towards society (Zhang et al.,
2022).
9
• Society demands more responsible and sustainable approach for
business practices
• Corporations must re-evaluate their social and environmental
performances (Kirst et al,, 2021)
• Embedding social responsibility in a company’s DNA by combining
mission of growing financially with attitude of being conducive to
improved sustainability performance (Mion et al., 2023)
• B-Corp certification increases transparency, accountability, and
sustainability
• KIND and other companies are examples of profit and purpose
• Environment and society of tomorrow is protected when
companies use sustainable business practices today
10
Potential Research Topic, Method, Design,
Participants, and Research Question
Topic, Method, Design: A Qualitative Case-Study on how Finance Companies in Latin
America can Contribute to Social Development by Obtaining a B-Corp Certification.
Participant(s): 400 finance companies in Latin America that have or will earn a B-Corp
certification from B-Lab.
Research Question: What specific actions could finance companies that have or will
earn a B-Corp certification from B-Lab perform to innovatively contribute to social
development in their respective countries?
Comp Exam Question: B-Corps balance their social-environmental missions along with
financial goals to trigger social and environmental development (Gamble et al., 2020). A Chilean
qualitative study of 425 companies in Latin America and the Caribbean revealed that the COVID19 global pandemic and economic situation has advanced the adoption of the B-Corp business
model demonstrating a stronger focus on social welfare, economic growth, and preservation of
natural resources (Acevedo et al., 2024). Corporation could see a country as a brand, and by
incorporating sustainable business practices into their processes, the companies could create or
add social value while meeting their economic objectives (Cantele et al., 2023). What specific
actions could finance companies that have or will earn a B-Corp certification from B-Lab
perform to innovatively contribute to social development in their respective countries?
11
12
Example of Lecture Slide Deck Organization and Content for
Topic, Research Plan, and Comp Exam Question
WARNING: Do not use these. Do not copy these. DO write something that aligns with your
lecture topic.
Example of a topic: Achieving B-Corp Status as an Innovative Business Model
Lecture slides might include:
Slide 1: Achieving B-Corp Status as an innovative business model.
Agenda
Slide 2. What is a certified B-Corp (compared to benefit corp)?
Slide 3. History of the B-Corp and B-Lap initiatives
Slide 4. B-Corp advantages, disadvantages, and challenges.
Slide 5. Examples of B-Corps
Slide 6. Daniel Lebowtski and the KIND story
Slide 7. Small businesses as B-Corps
Slide 8. Large Corporations as B-Corps
Slide 9. How to establish a B-Corp
Slide 10. Potential Research Topic
Note – the highlighted ideas are just the main idea for each slide – we have not provided you
with full slide-deck examples for Slides 2-9. Use your creativity for yours.
Slides 10 and 11 are provided in full as examples (do not use the examples).
Slide 10: Potential Research Topic, Method, Design, Participants, and Research
Questions
Topic, Method, Design: A Qualitative Case-Study on a Small Business That
Successfully Completed B-Lab’s B-Corp Certification Process.
Participant(s): A small business that recently earned B-Corp certification
from the B-Lab.
Example Research Questions:
RQ 1: How do small business owners strategically and succesfully
complete the B-Corp certification process?
RQ 2: How do small business owners establish appropriate
sustainability measures for meeting the B-Corp certification
requirements?
Slide 11: Example Comp Exam Question
Barry and Jen decided they were interested in creating a new sustainable business
where they used goat’s milk to make cheese, yogurt, and baby formula. They wanted
to create a business strategy that would allow them to make the most of their strategic
plan to initiate the process of applying for and receiving B-Corp Certification. In a
five-paragraph essay that includes an introduction with a thesis sentence, at least three
organized paragraphs with headings, and a final conclusion that includes
recommended “next steps,” provide Barry and Jen with an overview of the B-Corp
process and how they should start thinking about their business strategy which would
help them maneuver through the B-Corp process. What ideas might you provide
about specific ideas and included topics for their new business strategy?
Note. Your grade on the writing of the exam question will be based on how well it aligns with
the content of your lecture. Can someone review your lecture and handout and have enough
information to write a well-considered exam question (considering that the classmate will have
also taken this course)? However, you also should not provide the ANSWER to the question.
Give them enough that they can answer but not an actual sample answer.
Studies in Educational Evaluation 69 (2021) 100842
Contents lists available at ScienceDirect
Studies in Educational Evaluation
journal homepage: www.elsevier.com/locate/stueduc
Misconceptions about data-based decision making in education: An
exploration of the literature
Ellen B. Mandinach a, Kim Schildkamp b, *
a
b
WestEd, United States
University of Twente, Netherlands
A R T I C L E I N F O
A B S T R A C T
Keywords:
Data-driven decision making
Data literacy
Data use
Accountability
Continuous improvement
Theory
Misconceptions
Teacher preparation
Data-based decision making
Research on data-based decision making has proliferated around the world, fueled by policy recommendations
and the diverse data that are now available to educators to inform their practice. Yet, many misconceptions and
concerns have been raised by researchers and practitioners. To better understand the issues, a session was
convened at AERA’s annual convention in 2018, followed by an analysis of the literature based on mis
conceptions that emerged. This commentary is an outgrowth of that exploration by providing research, theo
retical, and practical evidence to dispel some of the misconceptions. Our objective is to survey and synthesize the
landscape of the data-based decision making literature to address the identified misconceptions and then to serve
as a stimulus to changes in policy and practice as well as a roadmap for a research agenda.
1. Background
1.1. The impetus
Data-based decision making (DBDM; or data-driven decision mak
ing), data use for short, has emerged and evolved as a key field in ed
ucation for nearly two decades. DBDM has become important, in part,
because policymakers have stressed the need for education to become an
evidence-based field, causing educators to rely more on data and
research evidence, and not just experience and intuition. Accordingly,
research on DBDM has paralleled policy mandates and emerging prac
tice. With any evolving field, however, intent can sometimes be
confused and misused or poor implementation can occur. Mis
conceptions about DBDM have arisen over the course of changing
mandates from practice, policy, research, and theory. This article is
meant to be a commentary that is grounded in the DBDM literature. The
purpose of the article is to identify and address the misconceptions
through the presentation of relevant research that either confirms or
disconfirms the basis of the misconceptions.
1.1.1. Definitions and background
We begin with some basic definitions and background to assist the
reader. Definitions for the process of data use differ as do emphases.
Based on a review of over 3000 reports and journal articles, Hamilton
et al. (2009) define DBDM as the systematic collection and analysis of
different kinds of data to inform educational decisions. Mandinach and
Gummer (2016b) make clear the importance of considering diverse data
sources in the decision-making process because all too often, educators
think only of student performance indices (i.e. assessment results) as
educational data. This is a main issue raised among the misconceptions
later in the paper. Research finds that effective data use requires the use
of multiple sources of qualitative as well as quantitative data, and not
solely achievement data (Lai & Schildkamp, 2013; Mandinach & Gum
mer, 2016b). Data use is a complex and interpretive process, in which
goals have to be set, data have to be identified, collected, analyzed, and
interpreted, and used to improve teaching and learning (Coburn, Toure,
& Yamashita, 2009; Coburn & Turner, 2011; Mandinach & Jackson,
2012).
This interpretive transformation process involves a comprehensive
skill set as part of an iterative inquiry process that informs decision
making. DBDM was described by Van der Kleij, Vermeulen, Schildkamp,
and Eggen (2015) as a formative assessment approach, as assessments
are used to support learning, evidence is gathered, interpreted and used
to change the learning environment based on students’ needs (Van der
Kleij et al., 2015; Wiliam, 2011). Data use, in part, originated in the
United States as a consequence of the No Child Left Behind (NCLB) Act,
* Corresponding author.
E-mail addresses: emandin@wested.org (E.B. Mandinach), k.schildkamp@utwente.nl (K. Schildkamp).
https://doi.org/10.1016/j.stueduc.2020.100842
Received 20 September 2019; Received in revised form 15 January 2020; Accepted 19 January 2020
Available online 23 January 2020
0191-491X/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
E.B. Mandinach and K. Schildkamp
Studies in Educational Evaluation 69 (2021) 100842
and has continued in the Every Student Succeeds Act (ESSA) in which
learning outcomes were defined in terms of results and attaining spec
ified targets (Wayman, Spikes, & Volonnino, 2013). This stimulated the
use of data for informing teaching and learning in schools in the United
States (Wayman, Jimerson, & Cho, 2012), but most often, for the pur
poses of accountability and compliance, rather than for continuous
improvement (Hargreaves & Braun, 2013).
Policymakers have stressed the importance of data use to make ed
ucation an evidence-based discipline. Researchers are studying diverse
aspects of how data are being used in education and the impact.
Although DBDM has existed for many years, some practitioners still
maintain that it is a passing fad. Yet we maintain that effective educators
have used data to inform their practice for a long time but many things
in education have changed to position data use differently. These
changes can even be connected to the learning theories behind data use.
Van der Kleij et al. (2015) analyzed the learning theories in which data
use is grounded. They state that early initiatives of data use were based
on neo-behaviorism and cognitivism (Stobart, 2008), with no explicit
attention paid to the environment in which the teaching and learning
occurred. According to Van der Kleij et al. (2015) data use focused on
reaching preset attainment targets, checking if these targets have been
reached, and adapting the learning environment where needed. The
focus was on teachers using assessments to check on individual student’s
abilities and delivering adequate instruction to these students. This
focus is aligned with a focus on accountability and compliance in such
policies as NCLB and ESSA in the United States where the subgroup
reporting requirements sought to promote equity. Data use for
accountability continues to be a prominent focus due to federal and state
and/or national testing and compliance policies (Hargreaves & Braun,
2013; Nichols & Berliner, 2007). However, this focus did not take the
variety of contexts in which the learning occurred into account, and also
led to a narrow focus on achievement data, as the sole source of
important data.
In the last decade a change from a sole focus on accountability to an
emphasis on continuous improvement occurred (Mandinach, 2012).
According to Van der Kleij et al (2015, p. 330), data use has moved
towards a more sociocultural paradigm as it is currently ‘focusing on
continuously adapting learning environments to facilitate and optimize
learning processes, taking into account learners’ needs and individual
characteristics. Thus, instead of just acknowledging the context or
controlling for it, the emphasis is on the process of data use within a
particular context (Coburn & Turner, 2011; Schildkamp, Lai, & Earl,
2013; Supovitz, 2010).
This shift toward continuous improvement has important implica
tions for data use because many criticisms have emanated from the
pressures of data use for accountability. The criticisms will be explored
below. Second and relatedly, research recognizes that students and their
backgrounds and circumstance are complex and face situational chal
lenges that require educators to tap diverse data sources to gain a
comprehensive understanding of their students (Datnow, 2017; Datnow
& Park, 2018). This means that a reliance only on test scores and in
dicators of student performance is inadequate. Educators need data,
such as demographics, attendance, health, transportation, justice,
motivation, home circumstances (i.e., homelessness, foster care, po
tential abuse, poverty), and special designations (i.e., disability, lan
guage learners, bullying) to contextualize student performance and
behavior. These other sources of data are not intended to replace
essential data around student performance, but to provide explanations
and context to help educators better understand and interpret what the
data mean (Mandinach, Warner, & Mundry, 2019). Third, sophisticated
technologies and apps now exist that enable educators to access and
make effective use of diverse sources of data to improve the quality of
educational decision making.
educational problems, but data use is not a solution to all problems. Like
everything, the effectiveness of DBDM depends on many factors,
including the intent of use. Critics are vocal about their concerns (Penuel
& Shepard, 2016).
For some DBDM seems to be an untenable, unsubstantiated, and ir
rational enterprise. Why, then are there policies, pressures, and em
phases on data use, given the challenges and criticisms? Clearly there are
opportunities and affordances provided by data use. The literature has
been growing as data use has become more embedded into practice.
Research on DBDM finds that the components of data use can either
serve as an enabler or hinderance (Jimerson, Garry, Poortman, &
Schildkamp, 2019). If done well, data use can be a positive activity.
Conversely, if done poorly, it is a hinderance.
Educators (and other critics) sometimes wonder if data use makes a
difference in educational practice. They ask if there is evidence of
impact. Evidence to support or refute the claims can be found in
research. For example, several studies show that if used effectively, data
use can lead to increased student achievement (Marsh, 2012). Schild
kamp, Poortman, Ebbeler, and Pieters (2019) conducted a literature
review and identified 11 data use interventions that have been studied
scientifically, and where there was evidence for an impact on student
achievement. All of these interventions were studied scientifically, and
all studies provide more evidence than just anecdotal evidence. Most of
the studies included in their study used a quasi-experimental or ran
domized control trial design. This confirms that if done well, it can
actually lead to improved student learning and achievement.
Clearly DBDM must address both accountability and continuous
improvement objectives but there must be a balance. Teachers worry
about the ramifications or the gotchas that data use might have in terms
of data use for accountability (Nichols & Berliner, 2007). There are
concerns about data (sic test scores) being used for inappropriate de
cisions, including teacher evaluations. This concern goes both for sum
mative data being used for formative purposes and the reverse. But as
Bennett (2011) and Pellegrino (2010) both note, the differences are not
necessarily distinct. We therefore invoke the wisdom of Cronbach
(1988) that validity really is about interpretation, not just about the
properties of the instrument or the data.
Given the controversies around DBDM, the explicit purpose of this
commentary is to lay out the topics of concern and misconceptions and
then explore the literature to address the challenges, opportunities, and
practices. To support the contentions of the commentary, we review the
literature and how the research addresses issues in DBDM, focusing on a
select number of topics. We conclude with logical steps to move the field
forward. It is our hope that this commentary will stimulate improvement
among colleagues within the data field while informing other colleagues
about DBDM.
1.2. Key misconceptions: what the literature says
The commentary is based on a range of relevant literature, including
but not limited to several review studies in this area (Datnow & Hub
bard, 2015; Hamilton et al., 2009; Heitink, Van der Kleij, Veldkamp,
Schildkamp, & Kippers, 2016; Hoogland et al., 2016; Schenke & Meijer,
2018). Our review of the literature yielded five key topics around which
criticisms or misconceptions about data use exist. We specify the con
cerns, misconceptions, and issues around the general topic and then
provide a review that relates to each topic. Using the landscape of the
literature and our knowledge of DBDM, we then posit recommendations
for the field to consider and to stimulate progress for those working in
the area of data use. These recommendations are geared primarily for
researchers but also for practitioners and policymakers. The general
topics addressed in this section revolve around: (1) theories of action
and the process of data use; (2) the goals of data use; (3) what constitutes
data and the data use process; (4) data literacy; and (5) technology. The
ordering of these topics is purposeful although the misconceptions are
systemically interrelated. We begin with theoretical issues and follow
1.1.2. Evidence of impact
Some proponents of DBDM believe that the use of data can solve
2
E.B. Mandinach and K. Schildkamp
Studies in Educational Evaluation 69 (2021) 100842
with infrastructure components.
Before we discuss the review, it is important to note that there, of
course, is diversity in practice and that nothing is universal. Nuances in
theory, research, and practice exist. The commentary attempts to pre
sent a balanced view of the literature. It also is important to note is that
much of the research has occurred in Europe (particularly the
Netherlands and Belgium) and the United States, and to a lesser degree
in New Zealand. Other countries are now engaging, and the research will
follow.
improved student achievement (large effect sizes, ranging from d = 0.54
to 0.66) (Poortman & Schildkamp, 2016).
Based on this sensemaking process, whether it is in a data team or
not, different types of improvement actions can be developed and
implemented, for example actions with regard to curriculum, instruc
tional and/or assessment changes (e.g., Gelderblom, Schildkamp, Piet
ers, & Ehren, 2016; Poortman & Schildkamp, 2016; Schildkamp et al.,
2016). After these actions have been implemented, data need to be
collected to determine whether or not the goals set in the beginning of
the process are reached.
Although described as a rather straightforward and linear process, in
reality educators move back and forward between these different steps
of the data use cycle, making it an iterative process. Moreover, data use
is not a straightforward and not even an exclusively rational process
(Bertrand & Marsh, 2015; Kahneman & Frederick, 2005), it also involves
professional judgement. For example, as stated by Schildkamp (2019), p.
8): ‘the same data might have different meanings for different people;
decisions can never be completely based on data, because people filter
data through their own lenses and experiences, in which intuition also
plays an important role (Greene, & Gannon-Slater 2017).’ Moreover,
confirmation bias plays a role, as people may try to fit data into a frame
that confirms their already pre-existing beliefs (Kahneman & Frederick,
2005; Kauffman, Reips, & Merki, 2016; Vanlommel & Schildkamp,
2018).
One more important aspect to consider in the process of data use is
that this process has multiple stakeholders. The focus is often on
teachers (and school leaders) using data, but in our view, students
should not only be the recipients here, but should be participants in the
process of data use. Hamilton et al. (2009) noted that students as
data-driven decision makers rose to the level of one of five recommen
dations in the Institute of Education Sciences’ Practice Guide. Students
can, together with teachers, examine their own test results (Kennedy &
Datnow, 2011; Levin & Datnow, 2012). Students need to be actively
involved in the data use process to enhance their commitment and
motivation, which in turn can lead to enhanced learning (Fletcher &
Shaw, 2012). However, a review study by Hoogland et al. (2016) into
the use of data concludes that the role of the student in the data use
process has not been studied much yet.
1.2.1. Data-driven decision making: theories of action and the process of
data use
1.2.1.1. Misconception 1: DBDM Interventions lack a theory of action. The
looming misconception or issue that pertains to the theories underlying
DBDM result from a criticism leveled by Penuel and Shepard (2016) that
data-driven interventions lack a theory of action. However, several
theories of action, conceptual frameworks, and models of inquiry exist
with regard to data use (e.g., Boudett et al., 2013; Coburn & Turner,
2011; Hamilton et al., 2009; Lai & Schildkamp, 2013; Mandinach,
Honey, Light, & Brunner, 2008; Marsh, 2012; Schenke & Meijer, 2018;
Schildkamp & Poortman, 2015). Data use often starts with a certain goal
that educators want to reach, usually related to improving the quality of
teaching and learning in the school (e.g., student learning goals,
aggregated achievement goals). It is essential that these goals are clear
and measurable (Hamilton et al., 2009; Schildkamp, 2019). Multiple
data sources can be used to determine whether these goals are reached.
Next, educators need to make sense of these data (Vanlommel, Van
Gasse, Vanhoof, & Van Petegem, 2017; Weick, 1995). Educators must
collectively analyze and interpret the data to identify problems (i.e.,
when the set goals are not being met) and possible causes of these
problems. Data use should not be an individual effort, collectively
engaging in this sensemaking process is crucial as the implications
regarding solutions to the problems and consequent actions based on the
analysis of the data are often not self-evident (Mandinach et al., 2008;
Marsh, 2012; Vanlommel et al., 2017). Collaboration may take place in a
data team. A data team is a group of educators who come together
around the examination of data to discuss actionable strategies. Data
teams have different compositions. They can be formed around grade
levels, content, or across grade levels and are led by a data coach (Far
ley-Ripple & Buttram, 2015; Huguet, Marsh, & Farrell, 2014; Schild
kamp, Poortman, & Handelzalts, 2016). The assumption is that teachers
collaborate through various kinds of teams. In these teams it is impor
tant to work toward continuous improvement and to try to address the
needs of individual students through collaborative inquiry (Datnow &
Park, 2018).
There are questions about whether data teams have the appropriate
skills and knowledge, what kinds of inquiry processes they are using for
what kinds of decision making, and if there are adequate structures and
resources to support the collaborative inquiry and sensemaking process.
Several studies have begun to answer these questions (Bocala & Boudett,
2015; Bolhuis, Voogt, & Schildkamp, 2019; Datnow, Park, &
Kennedy-Lewis, 2013). For example, the Data Team intervention
developed in the Netherlands, has been systematically researched to
evaluate and theorize the (effects of the) professional development over
an extended period of time in different countries (The Netherlands,
Sweden, Belgium, England, and the United States). In this intervention,
data teams consisting of six to eight teachers and school leaders
collaboratively use data to solve a selected educational problem within
the school. Research results (Ebbeler, Poortman, Schildkamp, & Pieters,
2017; Kippers, Poortman, Schildkamp, & Visscher, 2018) show that
school leaders and teachers developed the necessary knowledge and
skills to use data to improve education, an effect of the intervention on
data literacy was found (effect sizes ranging from d = 0.60 to d = 0.71.
Moreover, several data teams were able to solve their problem and
1.2.1.2. Recommendations. Based on the literature, we formulated the
following recommendations to stimulate progress in the field:
• Do not start with data, but with clear and measurable goals;
• Triangulate different data sources, to capture the needs of diverse
students;
• Collectively engage in a sensemaking process, for example in a data
team;
• Connect professional judgement and data use to increase the quality
of decision making;
• Involve students in the process of data use; and
• Conduct research into the role of students in the process of data use.
1.2.2. Goals of data use
1.2.2.1. Misconception 2: Data use is only for accountability purposes.
One of the main misconceptions is that data use is only for account
ability purposes. Much of the criticism towards DBDM is related to
accountability and compliance (Firestone & Gonzalez, 2007; Ingram,
Louis, & Schroeder, 2004). As Datnow and Park (2018) note, data use is
inextricably linked to accountability. Moreover, data use is often con
nected to two distinct goals: school improvement and accountability.
Tensions and conflicts have arisen between these different types of goals
(Hargreaves & Braun, 2013).
For example, Penuel and Shepard (2016) comment that there is a
narrow focus on raising standardized test scores as a primary goal for the
3
E.B. Mandinach and K. Schildkamp
Studies in Educational Evaluation 69 (2021) 100842
data-driven decision making interventions. One accountability related
misconception that we would like to address here is that accountability
pressure is conflated with data use because of the sole focus on test
scores rather than a broader view of diverse data sources that can inform
about the whole child and provide an asset-based perspective (Atwood,
Jimerson, & Holt, 2019; Garner, Kahn, & Horn, 2017; Mandinach &
Gummer, 2016b). This issue is also related to the next misconception.
Moreover, because of the narrow accountability focus, some teachers
use data superficially, supporting a deficit mindset. Data can confirm
assumptions, challenge beliefs, but can also reinforce expectations for
low achieving students (Bertrand & Marsh, 2015; Datnow, 2017). In
contexts with too much accountability pressure, teachers often focus
more on students’ deficits than their assets (Datnow & Park, 2018), and
fail to make meaningful instructional change (Garner et al., 2017). Data
in this context can even be used to marginalize or shame particular
students (Garner et al., 2017; Neuman, 2016). Data use is these cases
only focuses on achievement and not on learning. Accountability related
to high-stakes test scores restricts teacher creativity, response, and
dialogue, and provides a limited view of data to address short-term goals
(Au, 2007; Berliner, 2011; Datnow, Park, & Choi, 2018; Nichols &
Berliner, 2007).
When there is a strong focus on accountability data use can lead to
narrowed curricula (Au, 2007; Berliner, 2011; Diamond & Cooper,
2007; Lipman, 2004; Nichols & Berliner, 2007). because of the narrow
focus only on standardized assessment and achievement on a narrow set
of topics (e.g., literacy and numeracy (Berliner, 2011; Datnow & Park,
2018). The misconception important to address here is the idea that data
use should focus only on standardized assessment and should only focus
on a narrow set of topics. Data can be used not only for subjects as
science, English, and Mathematics, but can also be used for topics such
as arts, physical education, and wellbeing of students.
The accountability pressure can also manifest itself in cultures where
teachers feel the potential for retribution and punitive actions, shaming
and blaming, especially when their students do not meet expectations,
and therefore have little trust in data use (Datnow et al., 2013; Ingram
et al., 2004). As Bocala and Boudett (2015) note, it is important for
teachers to feel trust and safety in their data use, while using evidence
rather than anecdotes. Unfortunately, in high accountability contexts,
many educators have the misconception that accountability goals
outweigh doing what is best to help their students.
Furthermore, if there is too much accountability pressure, this often
leads to misuse of data, and even to abuse. Data use can lead to undue
attention on students just below the threshold in order to increase their
proficiency scores (Booher-Jennings, 2005). In this case teachers focus
on the “bubble-kids” (i.e., students just below the threshold of a certain
cut scores as a way of reporting proficiency levels) with the assumption
that they will then reach mastery (Booher-Jennings, 2005; Moody &
Dede, 2008). These teachers will focus most of their efforts on a specific
type of student who can help improve the school’s status on benchmarks
and accountability indicators. Other possible negative effects due to too
much accountability pressure include gaming the system, cheating on
tests to reach a certain benchmark or accountability indicators, teaching
to the test, excluding certain (weaker) students from a test, and even for
marginalization and encouraging low performing students to drop out
(Booher-Jennings, 2005; Diamond & Cooper, 2007; Ehren & Swanborn,
2012; Hamilton, Stecher, & Yuan, 2008; Schildkamp et al., 2019).
Accountability can demoralize teachers and pressure them to use inap
propriate kinds of data and use data inappropriately (Diamond &
Spillane, 2004; Hubbard, Datnow, & Pruyn, 2014; Schildkamp & Tedlie,
2008).
However, this does not imply that data should never be used for
accountability purposes. Accountability is needed as it makes a system
more transparent, and it can be connected to data use for school
improvement as data used in such a system can reveal aspects that need
improvement (Tulowitzki, 2016). Data use for accountability and data
use for school improvement are both needed. Earl and Katz (2006)
stated in this light: “Accountability without improvement is empty
rhetoric, and improvement without accountability is whimsical action
without direction” (p. 12).
We argue here that it is crucial that data use starts with a certain
school improvement goal and not a focus solely on accountability and/
or on the data available. Data use often focuses on student achievement
as an important goal, but schools have other school improvement goals
as well, such as the well-being of students, information literacy, and
student self-regulation skills. Measuring progress towards these goals
requires other data than the traditional test scores (Schildkamp, 2019).
Moreover, equity is becoming an increasingly important goal in ed
ucation. This means that educators consult diverse data sources to
examine the whole child, not just student performance indices. An eq
uity lens seeks to adopt an asset-based perspective which capitalizes on
student strengths, interests, and backgrounds (Datnow & Park, 2018).
By challenging beliefs and assumptions and carefully framing conver
sations, teachers can examine discrepancies, and hold high expectations
for all students. With an explicit equity goal and the use of culturally
responsive pedagogy (Ladson-Billings, 1995), data can help teachers
redress inequities by using meaningful cultural resources and students’
experiences (Athanases, Wahleithner, & Bennett, 2012; Datnow, 2017;
Diamond & Cooper, 2007; Garner et al., 2017; Mandinach et al., 2019).
The primary implication for data use is that educators need to access and
use not only student performance indicators but also contextual and
background information about students from which they can make more
informed decisions. Examining the context and the background of stu
dents provides rich data sources to help educators understand the cul
ture, the interests, and the strengths of students bring to the classroom.
The emphasis on assets rather than deficits may be a difficult mindset
shift for some educators but the intent, as noted above, is to prevent
educators from making predetermined and potentially inaccurate as
sumptions about a student based on group characteristics such as
disability, ethnicity, religion, home circumstance, socio-economic sta
tus, or even being an athlete (e.g., dumb jocks cannot learn).
Schools have different types of data available, some of which have
been collected for many years. The question is whether all these
different data sources still address their purposes. Society and schools
are changing, so some data sources might not be as valuable anymore or
may have been collected for the wrong purpose. It is important to
consider what the goal of the data collected is and why are these things
being measured (Tulowitzki, 2016). It is important to prevent goal
displacement (Lavertu, 2014), a situation where what we can measure
become our goals, instead of measuring what we value and believe our
goals should be. Furthermore, educators may have developed new goals,
and may need to think about new data to collect to monitor progress
towards these new goals (Schildkamp, 2019).
1.2.2.2. Recommendations. Based on the literature we have formulated
the following recommendations:
• Balance the use of data for accountability and continuous
improvement;
• Assume an asset-based model for data use rather than a punitive,
deficit approach that is based solely on accountability that tends to
further marginalize the most challenged students;
• An increase of student achievement is an important goal for data use,
but also focus on other important educational goals, such as wellbeing and equity; and
• Evaluate the data sources available in the schools and school sys
tems: Are all data sources still valuable, is there anything missing?
1.2.3. What constitutes data and the data use process
1.2.3.1. Misconception 3: data equal test results. Perhaps one of the
biggest misconceptions that surrounds DBDM in education is what
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constitutes data. Most practitioners immediately think about test scores,
in particular, those that are used in a summative manner. For many
critics, there is a sole focus on assessment data, with no consideration for
other sources of data (Bocala & Boudett, 2015; Firestone & Gonzalez,
2007).
Even assessment data are nuanced. The farther removed from
classroom practice, such as high-stakes testing, the less informative the
data. We argue that instructional decisions need to be more aligned to
local data rather than state results that are too removed from the
instructional process, with tests being aligned to the curriculum. Data
are also more than only test results. Data now must be diverse and both
qualitative and quantitative, including socio-emotional, attitudes,
behavior, and more. Educators need formal data; that is, systematically
collected data (Lai & Schildkamp, 2016), but they also need informal
data. Educators collect information on the needs of their students in
everyday practice, for example, by observing their students and by
engaging in conversations with their students. These data are often
collected quickly, “on-the-fly” (Heritage, 2018; Klenowski, 2009), and
have the potential risk of reverting to experience and intuition rather
than data. Such “on-the-fly” data, part of a formative assessment pro
cess, must be collected carefully and connected to student learning
goals, with the objective of providing constructive feedback to students
(Heritage, 2018). It is important that educators triangulate across a
variety of data sources. Formal data come with disadvantages, such as
that student learning cannot be captured in a single test score and a test
score does not readily translate into the cause of performance or what to
do instructionally. With the use of informal data there is a (bigger) risk
of confirmation bias (Bolhuis, Schildkamp, & Voogt, 2016; Farrell &
Marsh, 2016; Katz & Dack, 2013; Vanlommel & Schildkamp, 2018;
Vanlommel et al., 2017). Bertrand and Marsh (2015) note the possibility
that teachers will confirm their beliefs based on student characteristics
related to results. Yet, the positive use of data can serve to challenge
beliefs and minimize confirmation bias (Datnow & Park, 2018; Love,
Stiles, Mundry, & DiRanna, 2008). The intent is to promote the equitable
use of data.
The ethics of data use is implied and assumed but a skill set not
readily covered in pre-service or in-service settings (Mandinach &
Gummer, forthcoming). Ethical and responsible data use is foundational
to data literacy (Data Quality Campaign, 2014; Mandinach & Gummer,
2016b). Data ethics extends past prevailing laws such as the Family
Educational Rights and Privacy Act (FERPA) in the United States and the
General Data Protection Regulation (GDPR) in Europe, moving beyond
the protection of privacy and confidentiality of student data. Data ethics
incorporates such skills and knowledge as understanding data quality,
using multiple data sources, using valid data sources aligned to the
targeted decision, and drawing valid interpretations on the given data.
As noted above, the quest for equity also is a component in that it focuses
on addressing assumptions and mitigating confirmatory bias, all parts of
ethical data use.
A foundational concept of data use is that students cannot and should
not be summarized by one data point. Putting it simply, students are too
complex. Our perspective is that student performance data, form the
central source of data for teachers, but now teachers need surrounding
and contextual information from which to understand each student and
to inform how they can design instructional steps to help that student.
With the proliferation of homelessness, foster care, truancy, behavioral
issues, medical challenges, bullying, and other contextual constraints,
teachers must have access to those data as well to understand why
students might be performing poorly, in order to determine appropriate
courses of action. Such data support cultural responsiveness and social
justice (Datnow & Park, 2018; Skrla, Scheurich, Garcia, & Nolly, 2004).
Additionally, teachers need data on their own performance in the
classroom to be able to address gaps in their own instruction and
performance.
Although there are exceptions, research still depicts a somewhat
constrained view of data. The relevant studies all focus on student
performance indices. Teachers use different kinds of data for different
kinds of decisions (Little, 2012; Spillane, 2012), yet they may not be
using the most relevant data. For example, Farrell and Marsh (2016)
found that state test data were mostly used for grouping. Benchmark
tests were used for discerning patterns but were deemed untrustworthy.
Common assessments were more trusted and considered more reliable
but the most valued data were the classroom-specific data that were
most closely aligned to instruction and student work. This study pro
vided valuable insights but is limited due to only looking at student
performance data, not the full set of information.
When it comes to using data for instructional decision making,
several studies point to problems with regard to the actual use of
different data sources to improve instructional decision making (e.g.,
Hoover & Abrams, 2013; Olah, Lawrence, & Riggan, 2010). Problems
identified in different studies include: a lack of (access to) different types
of data (Schildkamp & Kuiper, 2010); failing to perform a deeper
analysis of assessment data to yield valuable instructional insights
(Hoover & Abrams, 2013); a focus on the use of assessment data or test
preparation and instruction to improve test scores, while failing to in
fluence teaching practice (Garner et al., 2017); superficial use of data for
accountability and triage purposes (Booher-Jennings, 2005; Garner
et al., 2017; Lai & Schildkamp, 2016); interim data helps teachers to
consider which students need help, but do not provide sufficient infor
mation about what to teach and how to teach it (Goertz, Olah, & Riggan,
2009); and interim assessment data alone do not help teachers to
develop a deep understanding of students’ learning of the specific con
tent and the misconceptions of students (Garner et al., 2017; Goertz
et al., 2009).
Several of these problems relate to an overreliance on assessment
data, but these problems are also related to a lack of ability to interpret
the data, and interpretation is what allows teachers to determine on
which data to act (Farrell & Marsh, 2016). Data must be transformed
into actionable knowledge as part of the decision-making process
(Mandinach et al., 2008). Thus, one challenge is how to transform the
data into actionable steps that create lasting instructional impact, rather
than cursory reteaching and repetition.
The findings from most of these studies indicate that teachers often
use incomplete or even the wrong data for the kinds of decisions with
which they are confronted. They do not use a full range of data to gain a
comprehensive view of their students (Mandinach & Gummer, 2016b,
2016c). As Baker, Linn, Herman, and Koretz (2002) noted, student
achievement data may be primary, but it is essential to also have addi
tional data about student characteristics to contextualize student per
formance. Having a comprehensive view of students has become
increasingly important in light of the need to attend to diversity and
equity (Datnow & Park, 2018; Park, St. John, Datnow, & Choi, 2017).
We provide an illustrative example that does not focus on student
performance. One recent study (Atwood et al., 2019) is a model for why
educators sometime need to look beyond traditional or typical data
sources to make decisions that impact students. In many instances de
cisions focus on instruction and how to improve student performance
but other times, DBDM extends beyond student performance and the
need to access and examine a broader spectrum of data. The Atwood and
colleagues study examined how a school dealt with food insecurity
based on what was apparently a student’s theft of food. At a glance,
educators might have thought the student in question had behavioral
issues. But when looking more deeply and triangulating data, the edu
cators realized the student was hungry, with food insecurity, and the
family needed assistance. The school was able to mobilize a strategy to
help this student and others like her by examining diverse data sources
and not just making the most apparent interpretation from the most
obvious information.
In sum, when data use is more broadly focused on multiple measures
and a formative perspective, for the goal of addressing the whole child,
to improve instruction and learning, inform educational decisions, and
reflect on practice, data use can be powerful tools for continuous
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improvement. The whole child perspective may be particularly impor
tant in developing countries as reflected by a special issue of the Journal
of Professional Capital and Community that focuses on building capacity
through the use of a systems approach and evidence to inform policy and
practice.
Mandinach and Gummer (2013) have long argued that there is a lack
of human infrastructure for data use that must be addressed as early as
possible in an educator’s career, starting at the pre-service level and
carrying forward. As Means, Padilla, and Gallagher (2010) note, pro
fessional development around data must be ongoing and sustained.
Unfortunately, that is not the case in the United States as professional
development around data use tends to have a low priority and therefore
is not well addressed. Recent attention to data literacy in teacher
preparation programs indicates its growing importance (Mandinach &
Gummer, 2016c; Mandinach & Nunnaley, 2017; Reeves, 2017). In a
survey with large and representative sample of educator preparation
programs, the programs reported that they are teaching data literacy
(Mandinach, Friedman et al., 2015). That said, an in-depth analysis of
course syllabi from the study indicated that programs focus on assess
ment literacy rather than data literacy, although it is possible that some
non-responding programs may indeed teach about data literacy. In a
second survey of administrators and faculty of teacher preparation
programs, results indicated that the institutions want data literacy in
tegrated into pre-service curricula (Mandinach & Nunnaley, 2017).
At the in-service level, educators often do not have adequate support
in their schools with resources such as data coaches or data teams
(Jimerson et al., 2019; Lai & McNaughton, 2013; Schildkamp & Kuiper,
2010; Schildkamp & Poortman, 2015). Professional development can
improve data skills, often embedded within a content domain. There are
few comprehensive models of professional development that have been
scientifically developed and examined (e.g., Boudett et al., 2013; Lai
et al., 2014; Love et al., 2008; Schildkamp et al., 2018). For example, the
Using Data model (Love et al., 2008) creates data coaches and data
teams and strives for educators to attain 13 high-capacity data strate
gies, one of which is a focus on designing culturally proficient instruc
tional strategies to address equity and diversity issues. Such a focus has
the potential to creative more equitable education for all students
through the use of data (Datnow & Park, 2018). But the issue is
combining that knowledge with pedagogical content knowledge to
determine the needed instructional steps. Data literacy requires more
than just identifying which students need help, but also identifying
students’ learning needs. Data literacy is also more than just being
trained to use a particular data system, data-related application, or
assessment system. Further, any professional development or training
should focus on the actual use of data, not just the technical skills. It
should help the teachers change their practice by learning how to
transform data into actionable instructional steps while integrating their
knowledge of content and pedagogy. For example, Kippers et al. (2018)
studied the process of taking educational action based on data. The
study found that throughout the complex process of taking educational
actions, teachers need to combine their skills to use information with
their expertise about teaching and (their) students. Without data,
teachers do not know the gap between students’ current learning and
their learning goals, and without expertise, teachers do not know how to
close this gap (Mandinach & Gummer, 2016a).
1.2.3.2. Recommendations. Recommendations that can be made based
on the literature summarized above are:
• Acknowledge that data are diverse and that it is important to look
beyond traditional indices;
• Use a combination of formal and informal data in the decisionmaking process; and
• Align different types of data to the different kinds of decisions that
need to be made to make actionable decisions to inform practice.
1.2.4. Data literacy
1.2.4.1. Misconception 4: data literacy equals assessment literacy. The
major misconception is the conflation between data literacy and
assessment literacy where stakeholders do not understand the differ
ences, but the differences are very real and important for research,
theory, and practice (Beck, Morgan, & Whitesides, 2019; Mandinach &
Gummer, 2011; Mandinach, Kahl, Parton, & Carson, 2014).
Research shows that educators struggle with the use of data. With the
proliferation of data, educators are often overwhelmed and need to have
strategies for culling through the mounds of data (Hamilton et al.,
2009). The often-used phrase, drowning in data, is a reality. Many ed
ucators do not feel comfortable using data (Piro, Dunlap, & Shutt, 2014).
Educators, for example, struggle with setting clear and measurable
goals, collecting data, and making sense of data (e.g., Gelderblom et al.,
2016; Schildkamp et al., 2016). They struggle to identify problems of
practice and pose researchable questions (Means, Chen, DeBarger, &
Padilla, 2011). Moreover, they may not understand how to use data
effectively and responsibly, without violating student privacy and
confidentiality (Mandinach, Parton, Gummer, & Anderson, 2015). Ed
ucators sometimes fail to conduct the right types of analysis, and even
more often they have difficulties with connecting the data to their own
instruction in the classroom and translating the data into an action plan
(e.g., Brown, Schildkamp, & Hubers, 2017; Schildkamp & Kuiper, 2010;
Schildkamp & Poortman, 2015; Schildkamp et al., 2016). Further, the
lack of capacity can cause poor decisions and the misuse of data (Daly,
2012; Kahneman & Klein, 2009; Mandinach & Gummer, 2016b).
Having sophisticated technologies and appropriate data are foun
dational elements in data use, but educators must know how to use data
effectively and responsibly; that is, they must have some level of data
literacy (Data Quality Campaign, 2014; Mandinach & Gummer, 2016b,
2016c). A long-term concern remains that there is a lack of internal
capacity and a lack of adequate preparation at the pre-service or
in-service level beginning with assessment literacy (Mandinach &
Gummer, 2013; Mandinach, Friedman, & Gummer, 2015; Reeves &
Honig, 2015; Reeves, 2017; Schafer & Lissitz, 1987; Wise, Lukin, &
Roos, 1991) and morphing to the broader construct, data literacy. Data
literacy is seen as a broader construct in which educators use diverse
sources of data, not just assessments, to make informed decisions.
Mandinach and Gummer (2016b) define the construct:
1.2.4.2. Recommendations. Recommendations based on our literature
review include:
• Make use of the data literacy definition and framework that lays out
the skills, knowledge, and dispositions educators need to use data
effectively;
• Delineate the continuum of data literacy from novice to expert,
particularly identifying what the midpoints look like;
• Conduct more research on how the data skills interact with content
knowledge and pedagogical content knowledge (Mandinach &
Gummer, 2016b);
• Re-design the programs for the preparation and training of current
and future educators by introducing and integrating data literacy
into the curricula; and
Data literacy for teaching is the ability to transform information into
actionable instructional knowledge and practices by collecting,
analyzing, and interpreting all types of data (assessment, school
climate, behavioral, snapshot, longitudinal, moment-to-moment,
etc.) to help determine instructional steps. It combines an under
standing of data with standards, disciplinary knowledge and prac
tices, curricular knowledge, pedagogical content knowledge, and an
understanding of how children learn. (p. 14)
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• Recognize the importance of the merger of the culturally responsive
pedagogy with data literacy (Mandinach et al., 2019) to take a whole
child perspective and an equity lens while assuming an asset-based
model (Datnow & Park, 2018).
policymakers have urged the field of education to become more
evidence-based. In no way is the use of data a panacea or the sole source
of information to inform practice. Educator experience and professional
judgement count, but must be used in conjunction with data, especially
now that understanding students has become more complex. This means
that the data use field needs to move from neo-behaviorism and cogni
tivist perspective on data use to a more social-cultural paradigm. The
focus should be continuously adapting instruction in the classroom and
beyond, to facilitate and optimize students’ learning processes, taking
into account learners’ needs and individual characteristics.
This increasing complexity of students, their backgrounds and cir
cumstances should be an impetus for the use of a broad definition of data
use that includes all types of qualitative and quantitative data, formal
and informal data. It is essential to consider the whole child with diverse
data sources that go beyond traditional, quantitative student perfor
mance measures. This need also impacts educators’ skill sets to move
from assessment literacy to the broader conceptualization of data liter
acy (Data Quality Campaign, 2014; Mandinach & Gummer, 2016b;
Mandinach et al., 2014). As Bocala and Boudett (2015) note, “The goal is
not just getting teachers to be comfortable with data but allowing the
profession to evolve to a place where understanding of data is thor
oughly integrated with the work of learning and teaching” (p. 8).
Of course, student learning and achievement are important, but the
extension of data to diverse sources may influence students and the
educational process. Adapting an equity lens may well be the most
important contribution that the DBDM field can make in education; that
is the shift to understanding the whole child, with context and other
variables helping to enhance the interpretation of student performance
through cultural responsiveness. There are implications for practice.
Educators will need to look beyond performance data to understand the
student. It will require an asset-based model that focuses on student
strengths, interests, and contexts. We recognize, however, that the flip
side of this equity lens is the potential for confirmation bias as discussed
above. That said, we firmly believe that one of the strengths of DBDM, if
done effectively, appropriately, and responsibly, is for data use to enable
educators to make more culturally sensitive and equitable decisions
based on their knowledge of their students and the contextual factors
that may impact them on a daily basis. This focus has implications for
how teacher candidates and current educators acquire competence with
data through educator preparation programs and professional
development.
We have stressed the need to focus on data use for continuous
improvement rather than for just accountability and compliance, a
major philosophical shift. No doubt data will always be used to some
extent to meet accountability requirements. However, there should also
be a foundation for data use to inform the improvement process,
whether at the student, classroom, school, district, or federal level. The
more closely tied data are to the target of improvement, the more
effectively progress can be monitored and action steps taken. This in
volves addressing proximal goals rather than a focus on distal
accountability objectives. Critics have argued that such continuous
improvement is more a business model derived from organization
learning (Senge, 1990) than it is an educational process. We disagree.
The use of data to inform educational improvement can provide a
roadmap and actionable steps to inform practice. Christman et al. (2009)
apply an organizational learning framework and collaborative inquiry to
data use. This framework includes an iterative process in which a
problem is identified and action steps outlined to address the problem
with the objective of continuous improvement. Data can be a source of
information for educators to help students learn, but also to help edu
cators to improve their own classroom processes, instructional actions,
and behavior. Firestone and Gonzalez (2007) note that data for
continuous improvement can address organizational learning and
instructional improvement using a long-term approach to improvement.
In this way, as also argued by Van der Kleij et al. (2015) data use can be
seen as an approach to formative assessment, where the focus is on using
1.2.5. Technology
1.2.5.1. Misconception 5: technologies to support data use are inadequate,
because the graphical representations these offer fail to present the data in a
meaningful way. Technologies to support DBDM have proliferated,
ranging from sophisticated data warehouses to apps on mobile devices
(Means et al., 2010; Wayman, Cho, & Richards, 2010). However, edu
cators may not have technologies aligned with their educational ob
jectives or may have technologies that generate information that leads to
overly simplified or ill-conceived interpretations that are misleading
(Kahneman & Klein, 2009; Wayman et al., 2010). Some people (e.g.,
Penuel & Shepard, 2016) even accuse the data-based decision making
movement of “selling out to the vendors.” One of Penuel and Shepard’s
biggest complaints is that the developers of assessment systems produce
reports that summarize results into red, yellow, and green categories
that indicate to the user which students are failing, borderline, or
passing (Mandinach et al., 2018). It is called the stop light. According to
Penuel and Shepard’s comments at a large AERA session, this prob
lematic form of presentation oversimplifies the results and fails to pro
vide a roadmap for instructional steps. However, these categories may
provide a starting point for further analysis in each sub-group.
New technological opportunities arise almost every day and there
are many technologies that do not use the stop light approach. Tools are
improving and becoming more sophisticated in collecting and storing
(real time) data, and in visualizing and analyzing these data (e.g., data
warehouses, dashboards, data lockers, data analytics, data mining tools,
machine learning), moving beyond the stop light categories. Take for
example, data use in personalized learning environments. These envi
ronments make it possible for teachers and students to collect and have
access to diverse data sources supported by the interfaces with tech
nologies (Mandinach & Miskell, 2018; Pane, Steiner, Baird, & Hamilton,
2015). Further investments are needed in the design, development,
implementation, and evaluation of systems and tools that can support
teaching and learning in schools.
Two issues here are the lack of interoperability among the silos of
data and teachers’ knowledge of how to triangulate across the data
sources. The technologies to support data use need to attend to “high
tech” (e.g., the development of high-quality tools) and “human touch”
(e.g., making sure that educators can actually use these tools to benefit
the learner) (Schildkamp, 2019). To realize the potential of data use,
expertise is needed in the fields of technology (e.g., the vendors), as well
as in the field of learning and psychology, as data use is still mostly a
human endeavor (Schildkamp, 2019).
1.2.5.2. Recommendations. Recommendation with regard to technology
include:
• Use technology to support the data use process, but also engage in
further in-depth analysis;
• Invest in (connecting different types of) systems and tools that match
with the needs of the users; and
• Engage in educational studies to design, develop, implement, and
evaluate systems and tools that can support teaching and learning in
schools.
2. Conclusion and discussion: steps to move the field forward
Let us return to the original criticisms that motivated this review to
identify key themes and implications. Research and practice in the area
of DBDM has made great strides over the past two decades, as
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data to support student learning. But the educators need to know how to
make the data actionable; that is, they need to understand how to
translate the data into pedagogy or other actionable steps to address the
particular issues.
Effective data use also will shift the classroom to a more studentcentered environment, where students can become a vital part of the
educational process (Hamilton et al., 2009). Student involvement is a
fundamental principle underlying formative assessment (Heritage,
2010). Key steps were identified from the literature of ways to involve
students in the data process (Hamilton et al., 2009). First, by using data,
students can better understand performance criteria and expectations.
Second, the use of timely and constructive feedback based on data is an
essential part of the instructional process as is the provision of tools to
help students learn from the feedback. Finally, the review of data with
students will provide a better understanding of performance and may
motivate learning. However, we do need to acknowledge that the role of
the student in the data use process has received too little attention so far.
Only few studies have addressed the role of the student (Hoogland et al.,
2016). More research is urgently needed how to include students in the
process of data use, so that it leads to ownership, student learning, and
ultimately increased student achievement.
The effective use of data must be grounded in teacher beliefs (Dat
now and Hubbard, 2015; Prenger & Schildkamp, 2018) of the impor
tance of data use and data literacy (Data Quality Campaign, 2014;
Mandinach & Gummer, 2016b, 2016c). The acquisition of this skill set
and dispositions must be a lifelong learning process for educators. As
noted above, introducing data use to educators must begin during their
pre-service preparation and be reinforced throughout their careers
(Mandinach & Gummer, 2013; Mandinach & Nunnaley, 2017; Reeves,
2017). It must become an engrained part of practice, for example though
working in data teams (Schildkamp et al., 2018) and with knowledge
able data coaches (Love et al., 2008). We believe that working in teams
(e.g., grade level teams, subject matter teams) led by data coaches is the
way forward, as data use is a complex sensemaking process that does not
take place in isolation. It requires collective sensemaking and dialogue
(Schildkamp et al., 2016; Vanlommel & Schildkamp, 2018), focused on
the questions: What can we do as educators to help our students learn?
What are the actionable steps we can take to positively impact the
instructional process or affect better educational decisions?
We have reviewed some of the strategies around effective data use
that can provide the foundation and impetus for policy, practice, and
research. We urge the field to work toward a better understanding of the
actual data use process. We recognize that data use, if conducted
properly and in good faith with an equity lens can have a positive impact
addressing the needs of all students, regardless of circumstances. Taking
the equity perspective can impact how educators are prepared to use
data across the continuum of their careers. It can impact the focus of
courses in educator preparation programs as well as professional
development and in-service trainings. Finally, the practice field must
take seriously the need to develop data literacy in all educators, current
and future. This requires the mobilization of changes in the preparation
programs and the development of appropriate curriculum materials that
can be used (Mandinach & Nunnaley, 2017). We hope this commentary
will serve as a stimulus to change in policy and practice as well as a
roadmap for a research agenda.
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