However, all adaptive learning systems know who they teach (knowledge about the learner), what they teach (knowledge about the domain), and how to teach (knowledge about pedagogy)2<\/sup>.<\/p>\n An ideal ALS adapts itself in multiple ways. In the outer loop, the sequence of learning activities is adapted - similar to Youtube adapting recommended list of videos. The outer loop might also personalise learning approaches and difficulty levels.<\/p>\n In the inner loop, within each activity, the ALS monitors step-by-step progress. It adapts feedback and hints to correct misconceptions, if any. It might also point to additional content if the student has a problem remembering a previously learnt concept. Some experts argue that the inner loop is best left to the instructor: not only is it costly and time-consuming to program all the rules for the specific subject and task, but the teacher\u2019s knowledge and experience will always trump that of the machine3<\/sup>.<\/p>\n\n Like all recommendation problems (See How Youtube Learns You Part 1<\/a>), ALS splits the task into one or more surrogate questions that can be answered by the machine. Again, the choice of what to ask -and thus, what to predict, has a big impact on what recommendation is shown.<\/p>\n Marketing material often mention multiple goals \u2013 improved scores, employability and engagement. Given the proprietary nature of the systems, it is usually unclear what questions are coded into the systems, what goals are being optimised for, and how short-term goals are differentiated from long-term goals (example, mastery of a given content before progressing to the next level)4<\/sup>.<\/p>\n Where machine learning is used, regardless of the goals chosen, the prediction itself is based on other learners with similar skill levels and preferences. That is, learners whose models are similar.<\/p>\n\n For creating a student model, developers ask what student characteristics are relevant to the learning process. Unlike teachers who can directly observe their students and adjust their approach, machines are limited to the data that can be collected and processed by them.<\/p>\n Typical characteristics considered in a student model:<\/p>\n\n While this data changes and has to be recorded and updated, models also contain static characteristics such as age, gender, mother tongue and email identity2<\/sup>.<\/strong><\/p>\n Most of the ALSs create learner models based on interactions with students. Some also glean info from other sites, especially social media. Once a model is available for each learner, the machine calculates which students are similar to one another and estimate the probability that a given student will benefit from an activity, example or question3<\/sup>.<\/p>\n\n Learning objects can be text to read, a video, a set of problems, interactive activities (from simple fill-in-the-blanks to scenario-based learning activities), interactive animation, etc.1 <\/sup>The learning objects give what the learner needs to know, and the assessment activities indicate if the knowledge has been acquired3<\/sup>. The domain model contains all the features of the learning objects, including the associated KU and assessment.\nWhat a learner learns next will also depend on the inter-relationships between the KUs.<\/p>\n These KUs need to go into the model too: learning objects A and B might both be pre-requisites for learning object D. Thus, A and B have to be mastered before D. There is order amongst some KUs that tell us how we learn3<\/sup>. Conversely, if the student solves correctly a problem that corresponds to D, it would be a good bet that he or she mastered A and B too.<\/p>\n Subject-matter experts can provide some of these relationships. The other inferences can be learnt by the machine, which can predict the probability that a KU has been mastered. This mastery involves the system knowing that the learner has mastered A and B, given that he answered questions under D. It can then use this information, along with other features of learner and domain models, to recommend learning pathways and learning objects.<\/p>\n 1<\/sup> EdSurge, Decoding Adaptive<\/em>, Pearson, London, 2016.<\/p>\n 2 <\/sup>Alkhatlan, A., Kalita, J.K., Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments<\/em>, International Journal of Computer Applications 181(43):1-20, March 2019.<\/p>\n 3 <\/sup>Essa, A., A possible future for next generation adaptive learning systems<\/a><\/em>, Smart Learning Environments, 3, 16, 2016.<\/p>\n 4 <\/sup>Bulger M., Personalised Learning: The Conversations We\u2019re Not Having<\/em>, Data & Society Working Paper, 2016.<\/p>\n 5 <\/sup>Chrysafiadi, K., Virvou, M., Student modeling approaches: A literature review for the last decade<\/em>, Expert Systems with Applications, Elsevier, 2013.<\/p>\n 6 <\/sup>Groff, J., Personalized Learning: The state of the field and future directions<\/em>, Center for curriculum redesign, 2017.<\/p>\n 7 <\/sup>du Boulay, B., Poulovasillis, A., Holmes, W., Mavrikis, M., Artificial Intelligence And Big Data Technologies To Close The Achievement Gap<\/em>, In: Luckin, Rose ed. Enhancing Learning and Teaching with Technology. London: UCL Institute of Education Press, pp. 256\u201328, 2018.<\/p>","rendered":" When looking at an adaptive learning system, it is very hard to tell where it adapts1<\/sup>. What technology is used and what it is used for also changes across systems.<\/p>\n However, all adaptive learning systems know who they teach (knowledge about the learner), what they teach (knowledge about the domain), and how to teach (knowledge about pedagogy)2<\/sup>.<\/p>\n An ideal ALS adapts itself in multiple ways. In the outer loop, the sequence of learning activities is adapted – similar to Youtube adapting recommended list of videos. The outer loop might also personalise learning approaches and difficulty levels.<\/p>\nHow adaptive systems learn the learner<\/h3>\n
The learner model<\/h3>\n
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The domain model<\/h3>\n
![\"\"](\"http:\/\/aiopentext.itd.cnr.it\/wp-content\/uploads\/sites\/10\/2024\/01\/ch4-page5-learning-objects-1024x726.png\")
We can draw a loose parallel between learning objects in an ALS and videos on Youtube. A subject can be broken down into concepts and skills; these are called knowledge units (KUs) and are what the learner needs to know3<\/sup>. Each KU has a set of learning objects through which content can be learned, and a set of activities to assess learning. Some authors further break down learning objects into learning activities; we do not do this here.<\/p>\n![\"\"](\"http:\/\/aiopentext.itd.cnr.it\/wp-content\/uploads\/sites\/10\/2024\/01\/ch4-page-5-Granularity-1024x726.png\")
Other features of learning objects could include the activity\u2019s level of difficulty, its popularity and its ratings. The goal here, as in the case of Youtube recommendation, is to squeeze out as much information as possible from the data available available.<\/p>\n\n\n
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