Eliciting Adaptive Sequences for Online Learning

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Adaptive learning aims to provide efficient, effective, and customized learning paths to engage each student. The intent of adaptive learning systems is not only to use proficiency and determine what a student really knows but also to move students accurately and logically through a sequential learning path to attain the prescribed learning outcomes and skill mastery. These adaptive learning systems are quickly emerging but are still in the experimental stages. Although they have many benefits - such as providing greater time efficiency and focused remediation - it is challenging to develop them. Adaptively sequencing content and assessment is the core of an adaptive learning system. Existing adaptive sequencing approaches, such as the rule-based approach, the partially observed Markov decision process (POMDP) based framework, are either ineffective or difficult to develop. Recent advances in learning analytics, data mining, and machine learning (multi-armed bandit algorithms in particular), present new opportunities for building effective adaptive sequencing algorithms for online learning. The multi-armed bandit family of algorithms is named after a gambler who must decide which arm of a "multi-armed bandit" slot machine to pull to maximize the total reward in a series of trials. These algorithms are data-driven, can balance exploration and exploitation, and make sequential decisions under uncertainty. Thus, they are particularly relevant to decision-making about alternative pedagogies and lend themselves quite naturally to the problem of determining adaptive sequences in online learning environments. The objectives of this research are (1) to design a systematic methodology that includes a suite of algorithms based on the multi-armed framework that will work together with a hierarchical pedagogical model and a student model to produce adaptive sequences of knowledge components, learning activities, and formative assessment questions in an adaptive learning system. (2) to use simulations of students and courses to explore the performance of the designed algorithms and to optimize metrics or reward strategies; and (3) to develop a prototype of adaptive learning systems that will enable evaluations of the performance of the designed algorithms in real online courses. The long-term goal of this research is to enable future online learning systems to provide adaptively altering learning sequences of content and activities in real time that will best fit the student's needs and knowledge states. Such systems are expected to make student learning not only easier but also far more efficient.