{"id":34963,"date":"2018-11-13T18:37:38","date_gmt":"2018-11-13T23:37:38","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/blended-learning-the-rise-of-machine-learners\/"},"modified":"2018-11-13T18:37:38","modified_gmt":"2018-11-13T23:37:38","slug":"blended-learning-the-rise-of-machine-learners","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/blended-learning-the-rise-of-machine-learners\/","title":{"rendered":"Blended Learning: The Rise of Machine Learners"},"content":{"rendered":"

In 2010, only 54% students from grades 9-11 in the Dallas Independent School District (DISD) passed the math portion of the assessment test. In contrast, ~80% students from grades 3-6 met the minimum standard on Texas\u2019s assessment test. That means nearly half these students (~12000 in DISD) fell behind on basic math skills by high school.[1]<\/sup> Ideally, teachers could identify and prioritize the diverse needs of students, especially those who need intervention. However, resource constraints in school districts can seriously limit the personalized attention that students receive.<\/p>\n

In such cases, introduction of \u201cintelligent\u201d tutoring systems that use machine learning (ML) to gauge pre-existing knowledge, provide personalized tutoring and analyze performance, can dramatically improve learning outcomes. Reasoning Minds (RM), an adaptive video game that teaches math, does exactly that \u2013 A virtual \u201cgenie\u201d guides middle-schoolers through personalized math lessons where students progress through levels only after they\u2019ve mastered certain concepts.[2]<\/sup> Meanwhile, it alerts teachers if a student is stuck, allowing them to monitor individual progress and effectively intervene when needed instead of having to simultaneously field wide-ranging questions. Furthermore, students can access this learning platform (provided internet availability) even in the absence of teachers.<\/p>\n

\"\"<\/a>Figure 1: Sample guided study screen<\/em><\/p>\n

Given the promise of ML solutions, DISD decided to pilot RM in the short-term. ~2500 third and fourth graders were enrolled onto the platform in 2009 and another ~1000 in 2010. As the program demonstrated success, DISD expanded it to all grade 2 students in 2011, and to grade 3 students in 2012. [3]<\/sup> By 2016, ~33,000 students were enrolled for RM, adoption and usage continuing to flourish. Researchers measured 89% time on-task in RM when implemented with fidelity, potentially leading to up to 40 extra hours of instruction in a year.[4]<\/sup> Hence, it is no surprise that DISD teachers revealed strong support for RM, with 86% of supported and 71% of non-supported teachers stating RM benefits students, and 80% and 62% of supported and non-supported teachers willing to recommend it to others. [5]<\/sup><\/p>\n

Such ML-based tools that can \u201cpersonalize\u201d learning in classrooms (and outside) are expected to grow to a $6B+ world-wide market by 2024, with U.S. K-12 classrooms and consumers driving ~20% of that growth.[6]<\/sup> Tools like RM that customize content to individual learning patterns will continue to enhance student engagement and learning outcomes. In addition to the contribution to students\u2019 learning, ML offers significant advantages for teachers too. It can reduce their workload, allowing them time to focus on students one-on-one or on more complex problems. ML tools also provide teachers (and parents) with real-time feedback on students\u2019 progress, allowing them to intervene timely instead of waiting until periodic exams, when it might be too late.<\/p>\n

\"\"<\/a><\/p>\n

Figure 2: Expected growth of machine learning application in education<\/em><\/p>\n

For DISD specifically, as it extends usage of ML in the near-term, it should first assess the culture and technical readiness of schools before integrating these tools into traditional class formats and curricula. It must invest in technology infrastructure and standardize data sets (e.g. grade records) throughout the network to facilitate quicker implementation and adoption. While the technology adoption is at its nascent stage, DISD can also codify learnings from its RM programs thus far, defining best practices on how to efficiently leverage these tools for maximum benefit. Finally, DISD can start establishing strict security protocols in advance, as data privacy concerns are bound to arise.<\/p>\n

Longer term, as ML undertakes more responsibilities away from teachers, like time-consuming routine tasks of grading and record keeping, teachers\u2019 roles will have to evolve. Teachers will have to be trained in new skills, primarily developing a sophisticated understanding of technological tools so that they can utilize them effectively. To that effect, DISD should also start involving teachers in co-designing ML tools so that their proven pedagogical techniques can be embedded into these intelligent tutoring systems. This will also help DISD address teachers\u2019 fears of being replaced by technology and facilitate better adoption. On a more strategic level, as classrooms move towards a blended learning model – combining human and artificial intelligence, DISD will need to define a) the right balance between the two components, and b) the pace and stage(s) at which it introduces virtual teaching agents into students\u2019 lives.<\/p>\n

For schools to answer the above-mentioned questions, it will be critical to evaluate the design and impact of blended learning more holistically. Hence, I\u2019m curious to hear opinions on (1) the potential negative impacts, if any, of using ML tools on student learning, and (2) what if we flipped ML application in education on its head? Instead of machines learning how children think\/learn, what if these tools could proactively shape how children think – urging their curiosity and expanding their imagination?<\/p>\n

(785 words)<\/p>\n

References<\/strong><\/p>\n

[1] Dallas News (Dec 2011). \u201cDallas ISD to expand computer-based math program\u201d\u2013 Melissa Repko https:\/\/www.dallasnews.com\/news\/education\/2011\/12\/19\/dallas-isd-to-expand-computer-based-math-program<\/a>. Accessed on November 12, 2018<\/p>\n

[2] www.reasoningmind.org<\/a>. Accessed on November 11, 2018<\/p>\n

[3] Victor Kostyuk, Leigh Mingle, Steven Gaudino, Diedre Douglas (2015). \u201cReasoning Mind DISD 2015 Report\u201d. https:\/\/www.reasoningmind.org\/rmwp\/wp-content\/uploads\/sites\/4\/2015\/12\/2014-2015-Dallas-ISD-Report.pdf<\/a>. Accessed on November 11, 2018<\/p>\n

[4] Jaclyn Ocumpaugh, Ryan S.J.d. Baker, Steven Gaudino, Matthew J. Labrum, Travis Dezendorf (2011). \u201cField Observations of Engagement in Reasoning Mind\u201d, Springer-Verlag Berlin Heidelberg 2011. <\/em>Accessed on November 12, 2018<\/p>\n

[5] Bush, J. & Kim, M. (2015). \u201cEvaluation of Reasoning Mind: Final Report 2014\u201315\u201d. Accessed on November 13, 2018<\/p>\n

[6] Global Market Insights report (2018). \u201cArtificial Intelligence (AI) in Education Market worth over $6bn by 2024\u201d. https:\/\/www.gminsights.com\/pressrelease\/artificial-intelligence-ai-in-education-market<\/a>. Accessed on November 10, 2018<\/p>\n

Note: Supported teachers refers to those instructors who received additional training and implementation support from Reasoning Minds specialists<\/p>\n

 <\/p>\n

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