Anatomy of Student Models in Adaptive Learning Systems: A Systematic Literature Review of Individual Differences from 2001 to 2013

Abstract

This study brings an evidence-based review of user individual characteristics employed as sources of adaptation in recent adaptive learning systems. Twenty-two user individual characteristics were explored in a systematically designed search procedure, while 17 of them were identified as sources of adaptation in final selection. The content analysis of 98 selected publications that include evidence of adaptation efficiency is conducted. The quantitative representation of the findings shows current trends in the research of individual differences, as well as the tendencies of their further employment in student modeling. The article contributes to the body of knowledge on user individual differences and consequently to the research and development of adaptive learning systems. Additional contribution of the study is in-depth description of development and evaluation of the search strategy which *This article describes the results of research carried out within projects 177

The Effect of Pedagogical Hypermedia on Acquisition of Scientific Concepts among Primary School Students

“…This leads to more students understanding and acquiring scientific concepts, as they can access prerequisite, remedial, and enrichment material. The feature of control navigation allows students to organize individual processes that facilitate learning a complex and challenging science topic (Azevedo, 2005;Nakic, Granic, & Glavinic, 2015).…”

Adaptive Learning: A Stabilizing Influence Across Disciplines and Universities

“…The extensiveness of learning analytics data available as a student progresses through adaptive learning content allows for more granularity in identifying changes to their level of content knowledge. Researchers identified the ability to identify at-risk students early (Dziuban, Moskal, Cassisi, & Fawcett, 2016) and more precise measurement of learning that expedites mastery, improves course outcomes, and ultimately leads to increased student retention (Nakic, Granic, & Glavinic, 2015;Alli, Rajan, & Ratliff, 2016;Smith, 2013).…”

Effectiveness of an adaptive eLearning intervention on dental students’ learning in comparison to traditional instruction

“…12 The overall goal of AL and AeLS is invariably articulated throughout the literature as being more responsive to students as individual learners. 13 In Health Professions Education (HPE), innovative technologies, including adaptive digital learning, have increas-ingly been used to address instructional demands. [14][15][16] While the value of such technology seems to be demonstrated in medicine, 16 very few systems have been implemented and evaluated specifically for dental students.…”

“…Aleven and colleagues (2016) consider a learning environment to be adaptive when it is “adaptive to the degree that (1) its design is based on data about common learner challenges in the target subject matter, (2) its pedagogical decision‐making changes based on psychological measures of individual learners, and (c) it interactively responds to learner actions” 11 . With respect to an Adaptive eLearning System (AeLS), Stoyanov and Kirschner (2004) defined it as: “… an interactive system that personalizes and adapts e‐learning content, pedagogical models, and interactions between participants in the environment to meet the individual needs and preferences of users if and when they arise.” 12 The overall goal of AL and AeLS is invariably articulated throughout the literature as being more responsive to students as individual learners 13 …”