Developing an agent-based adaptive system for scaffolding self-regulated inquiry learning in history education

Abstract

This article presents a methodology for modelling the development of selfregulated learning skills in the context of computer-based learning environments using a combination of tracing techniques. The user-modelling techniques combine statistical and computational approaches to assess skill acquisition, practice, and refinement with the MetaHistoReasoning tool, a single-agent system that supports inquiry-based learning in the domain of history. Data were collected from twenty-two undergraduate students during a 4-h session where user interactions were logged by the system. A logistic regression model predicted user performance in relation to a skill categorization task with 75 % accuracy. The manner in which users apply the skills that are acquired is then assessed through a rule-based reasoning system that allows the pedagogical agent to adapt instruction. The results show that the model allows the agent to detect instances when skills are inappropriately applied as well as what type of goal that is pursued by students. We discuss the implications of these user-modelling techniques in terms of sequencing instructional content and using the tutoring agent to deliver several types of discourse moves in order to enhance learning.

The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: a meta-analysis

“…Adaptive scaffolding may be more beneficial for fostering SRL and lead to significant learning gains since it adjusts based on students' learning needs (Azevedo et al 2004. Previous studies have provided empirical evidence to support adaptive scaffolding in different learning domains, such as algebra (Kramarski and Hirsch 2003), biology (Azevedo et al 2004), and history (Poitras and Lajoie 2014). Adaptive scaffolds can flexibly fade based on the ongoing diagnosis and evaluation of students' knowledge and skills so that students ultimately internalize the learning process by themselves (Azevedo and Hadwin 2005).…”

“…Therefore, researchers designed and developed different kinds of tools using advanced technologies to scaffold SRL. For example, Poitras and Lajoie (2014) developed an agent-based adaptive system for scaffolding SRL in history education by tracing techniques. Lee et al (2010) found that a combination of generic learning strategy prompts with metacognitive feedback can promote learners' recall and comprehension in a computer-based learning environment.…”

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

“…The choices learners are able to make in ALTs Jarrell et al 2015a;Jarrell et al 2015b); (5) how to creatively solve a physics problem using a stylus in a 2D serious game (Newton's Playground; Shute et al 2013); and (6) how and when to use instructional tools such as a concept map to teach a virtual agent about environmental issues (Betty's Brain; Segedy et al 2013) or an annotation tool to evaluate the credibility of a historical document (Poitras and Lajoie 2014). These examples illustrate some of the many different types of choice learners are presented with while interacting with ALTs and their various learnersystem interaction parameters.…”

“…2) is available, it may be beneficial to provide the student the opportunity to review and be assessed on the material prior to proceeding to the next module. Poitras and Lajoie (2014) designed an ALT that provides a highly structured approach to teaching students about historical reasoning and how to interact with the system by having learners progress linearly through several practice modules that prepare them for the primary historical reasoning activity. This approach represents an example of non-adaptive proactive features and suggests that priming modules may be effective in helping learners progress more efficiently through the actual ALT content and perhaps more quickly to more advanced learning modules.…”

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

“…Under IRT, composite tasks are assigned probabilities in relation to each of the parameters a, b, and c. The combination of probabilities is combined with second measurement method known as cognitive diagnostics. Cognitive diagnostics is a psychometric modeling techniques used to develop profiles of cognitive systems used in task and skill mastery [15,18]. The combination of IRT and Cognitive Diagnostics converts all composite actions toward task completion into patterns of probabilities that are fed into the input nodes for the artificial neural network.…”