Imagine taking a microeconomics class when the course material in front of you at any time is instantaneously tailored to your knowledge base and gaps. You no longer will be bored out of your mind because the class is too easy, nor will you be so discouraged because the class is too hard. Moreover, you will only need to pay a quarter of what you would pay for an expensive textbook. Such tailor-made learning experience is what Knewton strives to deliver with machine learning.
Knewton is an adaptive learning education technology company, using machine learning to predict college students’ learning gaps and recommending personalized pedagogies to fill in these gaps. Knewton’s core product Alta, the company deemed as a textbook replacement, is entirely built upon machine learning technology. Although teachers have adopted individualized pedagogies for students’ different learning paths for a long time, machine learning enables such personalized learning to scale quickly through tools such as Alta.
There are three key features within a machine learning algorithm: “feature extraction, which determines what data to use in the model; regularization, which determines how the data are weighted within the model; and cross-validation, which tests the accuracy of the model”. Alta’s development and improvement encompasses all three key features:
First, Knewton team has initially worked with large publishers, such as Pearson, to collect and label learning data from their students, such as mouth clicks on concepts, wrong answers to specific questions etc. Through labeling millions of learning data, Knewton extracts features of students’ learning profile. Second, Knewton develops its “Knowledge Graph” in any given course, mapping out the relationships between the learning objectives and various competency features captured from learning data. During the regularization process, the algorithm determines which competency features are more likely to predict how much the learning objectives have been achieved. Based on progress on learning objectives, Alta predicts a student’s specific knowledge gaps and decides on which material a student should see next to address these gaps. Third, Alta cross-validates its predictions with past students’ competencies and predicted learning gaps based on the same data features and concludes whether its prediction is consistent with out-of-sample data.
As Knewton’s entire product is built upon machine learning, the key issues that the company is facing are all associated with features of machine learning technology. In the short term, Knewton needs to address student data privacy protection issue and needs to prove casual relationships between learning outcomes and Alta usage beyond mere predictions. First, Alta is predicated on collecting millions of learning data from students. Such data is sensitive and personal and requires rigorous protection. Knewton management has disguised all the student personal information, such as name, gender, and race. Second, machine learning is used to predict knowledge gaps based on students’ current knowledge, but the current algorithm alone cannot generate any causal relations about why students have these knowledge gaps. Moreover, there is no further evidence to prove that filling students’ knowledge gap using Alta can help students master their materials. To address this issue, the management has commissioned Johns Hopkins University’s Center for Research and Reform to study causal relations between students’ current knowledge base and knowledge gaps as well as between filling their knowledge gaps and mastering materials.
In the long run, Knewton plans to convince more stakeholders not only in higher education, but also in corporate training and K-12 schools to use Alta as a replacement for traditional course materials. Knewton believes that high-quality course materials are no longer exclusive to large textbook publishers. Knewton can source free or low-cost materials and develop the corresponding Knowledge Graph and adaptive learning algorithm to meet broader training and development needs. To attract broader potential clients, Knewton offers various service models, such as learning management system integration, to encourage more schools and employers to experience the product.
Currently Knewton functions best in domains with more fixed rules, such as accounting and finance. However, it has limited capacity for less standardized courses, such as management and creative writing. To better meet diverse customer needs in the short term, Knewton should refine its algorithm to accommodate subjects with more nuanced features and patterns. Moreover, the current product focuses exclusively on academic subjects while adaptive learning technology can also be applied to vocational trainings. In the long term, if Knewton wants to position itself to provide a wide range of personalized learnings, the team can consider developing algorithms for scalable and personalized vocational training, such as coding and user-interface design.
While adaptive learning based on machine learning has huge potential to improve students’ learning outcome and improve educators’ effectiveness, many questions remain: will Alta eventually eliminate the need for textbooks? How will traditional textbook publishers, such as Pearson, react to such product innovation?
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