{"id":34554,"date":"2018-11-13T18:13:08","date_gmt":"2018-11-13T23:13:08","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/enhanced-learning-through-machine-learning\/"},"modified":"2018-11-13T18:13:08","modified_gmt":"2018-11-13T23:13:08","slug":"enhanced-learning-through-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/enhanced-learning-through-machine-learning\/","title":{"rendered":"Enhanced Learning Through Machine Learning"},"content":{"rendered":"

Machine Learning, The DreamBox Solution:<\/em><\/p>\n

DreamBox, an education technology company based in Seattle, \u201cwas founded with the mission to transform the way the world learns by using intelligent adaptive technology to dynamically and continuously personalize each student\u2019s learning experience\u201d[1]<\/a>. In its essence, DreamBox uses machine learning technology to teach math to k-8 students through a personalized online platform. This platform is meant to be supplementary to the classroom experience. Dreambox uses data analysis to support its teaching process through Adaptive Learning Technology. According to the DreamBox website, \u201cthe Intelligent Adaptive Learning technology tracks each student’s interaction and evaluates the strategies used to solve problems. It then immediately adjusts the lesson and the level of difficulty, scaffolding, sequencing, number of hints, and pacing as appropriate.<\/em> This allows students, whether struggling, at grade level, or advanced, to progress at a pace that best benefits them and deepen conceptual understanding.\u201d[2]<\/a> <\/strong><\/em>DreamBox is solving a gap in the education industry by improving the math learning experience for elementary and middle school students. DreamBox CEO, Jessie Woolley-Wilson, says personalized learning can let \u201cstudents who are behind catch up and\u2026students who are ahead move forward. If the software notices that a student is using an inefficient procedure to solve a particular problem, it might cut in with a targeted lesson.\u201d[3]<\/a> \u00a0Machine learning is particularly important in improving DreamBox\u2019s processes, as it allows to 1) collect large amounts of data on\u00a0 how they interact with the math platform, and 2) based on this data, create a unique experience for students that could greatly impact their academic path. The more users interact with the platform, the more the platform learns about the users. DreamBox is solving a problem that requires prediction to develop the best possible learning track for a student by identifying patterns in responses and interactions with the platform. It is also serving to prove to educators that technology solutions can enhance the classroom experience.<\/p>\n

Next Steps:<\/em><\/p>\n

DreamBox recently received a $130 USD million investment from private equity fund TPG[4]<\/a>. These funds will be used in the coming years to increase access to math education around the world. [5]<\/a> In the short term, DreamBox will be focused on expanding its userbase and testing the platform in other geographies. According to a Seattle times article \u201cThe company has had pockets of success\u2026but what [Woolley-Wilson] really wants is significance \u2013 or proof and ability to expand the software to any school with repeated positive results.\u201d [6]<\/a> The short-term focus of DreamBox will be to prove the significance of results to date by expanding its userbase and geographic footprint, specifically into Asia, the Middle East, Africa, and Latin America.[7]<\/a> In the long term, Woolley-Wilson is looking to change how educators interact and feel about technology. She claims: \u201cthe skepticism educators feel about learning technology is justified. We have as an industry overpromised and underdelivered. And what that translates to is when there is something that actually works, that is engaging, that is personalized, and that is efficacious, people don\u2019t believe it.\u201d Over the long-term, she hopes to \u201cchange the narrative.\u201d[8]<\/a><\/p>\n

Recommendations:<\/em><\/p>\n

DreamBox\u2019s pursuit to prove significance in their results is extremely important for the near future. A 性视界 study on the use of DreamBox on student achievement found that while \u201cDreamBox progress measure was positively associated with achievement gains on state tests and interim assessments\u201d, \u201cthe evidence of causal impact\u2026is encouraging but mixed\u201d[9]<\/a>. It is critical for DreamBox to prove the causal relationship of their math platform and performance results. However, I would recommend DreamBox to begin exploring other fields in which to apply their technology. Even if DreamBox proves to be successful in the math space (which is a subject with little subjectivity in the types of responses a student can give to a question), their algorithms might not be applicable to other areas. With growing competition in the ed-tech space, DreamBox should be thinking ahead to become an education solution in more than one field. This would also aid Wolley-Wilson\u2019s objective to change the education narrative more broadly.<\/p>\n

Additional Questions:<\/em><\/p>\n

Upon reading about DreamBox and its CEO’s ambitions, some questions are raised: 1) Can machine learning in education be as successful without the support of a well-trained teacher and, therefore, could machine learning be the solution for regions in the world that lack sufficient teachers? Or will this type of digital solution always require a human component?; 2) Are there fields of education where machine learning would not add significant value or are all fields in education destined to eventually use machine learning in some way?\u00a0 (758)<\/p>\n

 <\/p>\n

[1]<\/a> Clare McGrane (2018) DreamBox Learning raises $130M, adds former U.S. Education Secretary to board, GeekWire. Available at: https:\/\/www.geekwire.com\/2018\/dreambox-learning-raises-130m-adds-former-u-s-education-secretary-board\/ (Accessed: 13 November 2018).<\/p>\n

[2]<\/a> What is Intelligent Adaptive Learning? (no date) DreamBox Learning. Available at: https:\/\/www.dreambox.com\/intelligent-adaptive-learning\/ (Accessed: 13 November 2018).<\/p>\n

[3]<\/a> Benjamin Herold (2018) \u2018What Does Personalized Learning Mean? Whatever People Want It To – Education Week\u2019. Available at: https:\/\/www.edweek.org\/ew\/articles\/2018\/11\/07\/what-does-personalized-learning-mean-whatever-people.html (Accessed: 13 November 2018).<\/p>\n

[4]<\/a>,[5]<\/a>,[6]<\/a> Rachel Lerman (2018) \u2018DreamBox Learning gets $130 million for math education software | The Seattle Times\u2019, 31 July. Available at: https:\/\/www.seattletimes.com\/business\/technology\/dreambox-learning-gets-130-million-for-math-education-software\/ (Accessed: 13 November 2018).<\/p>\n

[7]<\/a>,[8]<\/a>\u00a0 Ainsley Harris (2018) \u2018DreamBox Learning\u2019s adaptive math lessons get a $130 million boost\u2019, 31 August. Available at: https:\/\/www.fastcompany.com\/90208629\/dreambox-learnings-adaptive-math-lessons-get-a-130-million-boost (Accessed: 13 November 2018).<\/p>\n

[9]<\/a> Jon Fullerton (2016) DreamBox Learning Achievement Growth, Key Findings Report: DreamBox Learning Achievement Growth. Available at: https:\/\/cepr.harvard.edu\/dreambox-learning-achievement-growth (Accessed: 13 November 2018).<\/p>\n","protected":false},"excerpt":{"rendered":"

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