Vygotsky's notions of Zone of Proximal Development and Dynamic Assessment emphasize the importance of personalized learning that adapts to the needs and abilities of the learners and enables more efficient learning. In this work we introduce a novel adaptive learning engine called E-gostky that builds on these concepts to personalize the learning path within an e-learning system. E-gostky uses machine learning techniques to select the next content item that will challenge the student but will not be overwhelming, keeping students in their Zone of Proximal Development. To evaluate the system, we conducted an experiment where hundreds of students from several different elementary schools used our engine to learn fractions for five months. Our results show that using E-gostky can significantly reduce the time required to reach similar mastery. Specifically, in our experiment, it took students who were using the adaptive learning engine @math less time to reach a similar level of mastery as of those who didn't. Moreover, students made greater efforts to find the correct answer rather than guessing and class teachers reported that even students with learning disabilities showed higher engagement.
The ZPD in Vygotsky's theory is used to identify which psychological functions (and related social interactions) are needed for transitioning to the next age period, and to assess the current status of the child's maturing functions @cite_33 . Since performance in these psychological functions depends on social interactions, the assessment procedures should have dynamic, interactive nature. Hence, the ZPD provides a framework to evaluate learner's abilities, which is known as Dynamic Assessment (DA) @cite_33 @cite_13 . While in traditional approaches to assessment the main concern is current existing skills, DA is focused on measuring the learning potential for future development @cite_24 . A dynamic test is based on teacher assistance, where guidance, feedback and adaptive delivery of assistance are embedded in the evaluation procedure itself @cite_33 . Rather than information about past functioning and existing skills, dynamic approaches tend to be equally interested in estimating the learner's cognitive and meta-cognitive strategies, their responsiveness to assistance, and their ability to transfer skills that were learned with assistance to subsequent unassisted situations @cite_28 .
@cite_24 @cite_28 @cite_13 @cite_33
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This article reports the efforts of an elementary school teacher of Spanish as a second language to implement principles of dynamic assessment (DA) in her daily interactions with learners. DA is neither an assessment instrument nor a method of assessing but a framework for conceptualizing teaching and assessment as an integrated activity of understanding learner abilities by actively supporting their development (Poehner, 2008). DA is based on Vygotsky’s (1987) proposal of the zone of proximal development (ZPD), which underscores the developmental importance of providing appropriate support to learners to help them stretch beyond their independent performance. The particular approach to DA that the teacher followed reflected her interpretation of the ZPD as well as her knowledge of her instructional context and was arrived at through consultation with the present authors. In other words, her use of DA represents a unification of theory and practice, as advocated by Vygotsky, whereby theory offers a basis to guide practice but at the same time practice functions to refine and extend theory. Examples of the teacher’s interactions with learners in her classroom are discussed with regard to the opportunities for development they create. What kind of instruction is optimal for a particular child? Without doubt, this question is immediately comprehensible to any committed teacher in virtually any country in the world, and most teachers are likely to want concrete answers to the question, not only as a theoretical puzzle, but in relation to their immediate practices. If one were to look to scientific psychology and educational research for advice in relation to this practical problem, what would the answer(s) look like? This simple question raises several profound problems. Normative and political issues about the goals of instruction and the resources available for realizing these goals must be resolved. A theory of learning that can explain how intellectual capabilities are developed is needed. If instruction is not viewed as an end in itself, then a theory about the relationship between specific subject matter instruction and its consequences for psychological development is also needed. This last problem was the main tension against which Vygotsky developed his well-known concept of zone of proximal development , so that the zone focused on the relation between instruction and development, while being relevant to many of these other problems. Vygotsky's concept of zone of proximal development is more precise and elaborated than its common reception or interpretation. The main purpose of this chapter is to provide a comprehensive introduction to and interpretation of this concept, along with comments about predominant contemporary interpretations. The chapter concludes with some perspectives and implications derived from the interpretation presented here. We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children. Learning, planning, and representing knowledge at multiple levels of temporal ab- straction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforce- ment learning and Markov decision processes (MDPs). We extend the usual notion of action in this framework to include options—closed-loop policies for taking ac- tion over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as mus- cle twitches and joint torques. Overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning frame- work in a natural and general way. In particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic pro- gramming and in learning methods such as Q-learning. Formally, a set of options defined over an MDP constitutes a semi-Markov decision process (SMDP), and the theory of SMDPs provides the foundation for the theory of options. However, the most interesting issues concern the interplay between the underlying MDP and the SMDP and are thus beyond SMDP theory. We present results for three such cases: 1) we show that the results of planning with options can be used during execution to interrupt options and thereby perform even better than planned, 2) we introduce new intra-option methods that are able to learn about an option from fragments of its execution, and 3) we propose a notion of subgoal that can be used to improve the options themselves. All of these results have precursors in the existing literature; the contribution of this paper is to establish them in a simpler and more general setting with fewer changes to the existing reinforcement learning framework. In particular, we show that these results can be obtained without committing to (or ruling out) any particular approach to state abstraction, hierarchy, function approximation, or the macro-utility problem.