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• 1 2023-01-04T08:02:47+00:00 AI Speak : How Adaptive Systems Learn the Learner Part 2 1 plain 2023-01-04T08:02:47+00:00

  The Process

  In recent years, Machine learning is used more and more in adaptive systems, either as the only technology or in conjunction with other approaches.² Where used, the principle role of ML is creating and updating student models based on a set of features, including results from assessments and new data that is generated all along the process.¹
  
  For the outer loop, models are created with the help of training data, by assigning suitable weights features to help recommend effective learning content.² (Also refer how machine learning works.) These models are used to regularly recommend new learning paths that reflect student progress and changing interests - like new recommendations in Youtube. In ML based ALS, the number of pathways can run to trillions.³
  In the inside loop, Machine learning is used to give suitable feedback, spot errors, infer gaps in knowledge and assess mastery of Knowledge Units : While working on one activity, a student might make errors. ML can be used to predict what errors rise from which knowledge gap. If a step of the solution is correct, ML can be used to predict which knowledge units have been mastered successfully.²
  
  Other techniques used in adaptive systems involve less automation and more explicitly written rules for making inferences.² They demand a lot of programming time and extra effort to accurately capture all the critera that go into decision making. Further, the results cannot often be generalised from one domain to the next or from one problem to the next.
  
  Tools that use ML use large sets of data on actual student performance  and are able to create the most dynamic learning paths for students over time.¹ Like for all ML applications, there is training and testing to be done before being put to use in classrooms.
  

  Pedagogical model

  In the case of Youtube, we saw that there are a lot of value judgements on what makes a good recommendation - like how many user interests to cover in one set of recommendations, how many videos should be similar to already watched ones, how much new content to add for diversity (Refer How Youtube learns you part 2). ALS involves similar judgements on what it means to master a KU and how to get to that mastery : the pedagogy and daily experience of the learner.⁴
  
  In the case of ALS, these judgements and guidance on how a learner is to progress should be based on proven pedagogical theories. These go into the pedagogical model, and along with the domain and learner models, helps the machine choose an appropriate set of activities.
  
  Some of the questions answered in this model are : Should the student be presented a concept, an activity or a test next? At what difficulty level? How to evaluate the learning and provide feedback? Where is more scaffolding necessary? ⁵ (Scaffolds are support mechanisms that give guidance on concepts and procedure, the strategy used and on how to reflect, plan and monitor learning.) The pedagogical model dictates the breadth and depth of activities and even whether to continue within the ALS or get help from the teacher.³

  Interface

  The recommendations are presented along with other data like learner progress, performance and goals. The key questions here are:
  • How to deliver the content?
  • How much content to recommend in one go?
  • What is assigned directly and what is recommended?
  • What are the supporting resources?
  • Is it possible to provide group activities?
  • How much autonomy to permit?
  • Can the student change their preferences?
  • Can the teacher change the learning pathways?
  • What data is shown to the teacher?
  • Is the teacher in the loop?

  Evaluation

  When the ALS is put to use, most systems monitor their own performance against criteria set by the programmer. Like in any AI tool, data might be biased. Inferences drawn by the system can be imprecise. The student’s past data will become less and less relevant with time.⁶ Therefore, the teacher also has to monitor the system’s performance and provide learner guidance and corrective measures where necessary.
  
  It is also the teachers and peers who have to provide inspiration and reveal alternative resources :  Research in recommendation systems was shaped by commercial content providers and online retail companies for over a decade. Thus, the focus has been on reliably providing recommendations that produce results that can be promoted. “The surprising delight of an unexpected gem”⁷ and roads less travelled, that which can strongly inspire enduring learning, is not a strong point of machine based personalised learning.
  
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  ¹ EdSurge, Decoding Adaptive, Pearson, London, 2016
  ² Chrysafiadi, K., Virvou, M., Student modeling approaches: A literature review for the last decade, Expert Systems with Applications, Elseiver, 2013
  ³ Essa, A., A possible future for next generation adaptive learning systems, Smart Learning Environments, 3, 16, 2016
  ⁴ Groff, J., Personalized Learning : The state of the field and future directions, Center for curriculum redesign, 2017.
  ⁵ Alkhatlan, A., Kalita, J.K., Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments, International Journal of Computer Applications 181(43):1-20, March 2019
  ⁶ du Boulay, B., Poulovasillis, A., Holmes, W., Mavrikis, M., Artificial Intelligence And Big Data Technologies To Close The Achievement Gap, In: Luckin, Rose ed. Enhancing Learning and Teaching with Technology. London: UCL Institute of Education Press, pp. 256–28, 2018
  ⁷ Konstan, J., Terveen, L., Human-centered recommender systems: Origins, advances, challenges, and opportunities, AI Magazine, 42(3), 31-42, 2021