Correctness- and Confidence-based Adaptive Feedback of Kit-Build Concept Map with Confidence Tagging

In this paper, we present an adaptive feedback of Kit-Build concept map with confidence tagging (KB map-CT) for improving the understanding of learners in a reading situation. KB map-CT is a digital tool that supports the concept maps strategy where learners can construct concept maps for representing their understanding as learner maps and can identify their confidence in each proposition of the learner maps as a degree of their understanding. Kit-Build concept map (KB map) has been already realized the propositional level automatic diagnosis of the learner maps. Therefore, KB map-CT can utilize both correctness and confidence information for each proposition to design and distinguish feedback, that is, (1) correct and confident, (2) correct and unconfident, (3) incorrect and confident, and (4) incorrect and unconfident. An experiment was conducted to investigate the effectiveness of the adaptive feedback. The results suggest that learners can revise their maps after receiving feedback appropriately. In “correct and unconfident” case, adaptive feedback is useful to improve the confidence. In the case of “incorrect and confident,” improvement of the propositions was the same ratio with the case of “incorrect and unconfident.” The results of the delay test demonstrate that learners can retain their understanding and confidence one week later.This work was supported by JSPS KAKENHI Grant Number 17H0183901.'Artificial Intelligence in Education' 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part

An automated feedback system to support student learning of conceptual knowledge in writing-to-learn activities

As a pedagogical strategy, Writing-to-Learn (WTL) intends to use writing to improve students’ understanding of course content. However, most of the existing feedback systems for writing are mainly focused on improving students’ writing skills rather than their conceptual development. In this dissertation, an automatic approach is proposed to generate timely, actionable, and individualized feedback based on comparing knowledge representations extracted from lecture slides and individual students’ writing assignments. The novelty of the proposed approach lies in the feedback generation: to help students assimilate new knowledge into their existing knowledge better, their current knowledge is modeled as a set of matching concepts; suggested concepts and concept relationships for inclusion are generated as feedback by combining two factors, i.e., importance and relevance, of feedback candidates to the matching concepts in the domain knowledge. In the prototype system, a student can request feedback many times; each set of feedback is generated for a corresponding assignment draft to support their learning of conceptual knowledge during the iterative process of writing an assignment. This research conducts a repeated measures study across two semesters (N=88) to understand how students perceive the proposed system, explore how students use the automated feedback, and investigate the effects of the automated feedback on student learning. Survey results show that the feedback is perceived as relevant (78.4%), easy to understand (82.9%), accurate (76.1%) and useful (79.5%); survey results also find that the proposed system makes it easier to study course concepts (80.7%) and is useful in learning course concepts (77.3%). Based on the log analysis of students’ actual usage of the system, all participants request feedback at least once when using the proposed system. After requesting feedback, 83 out of 88 participants revise their assignments. Analyses of students’ submitted assignments reveal that more course concepts and concept relationships are included when completed using the proposed system. Collectively, these results show that the proposed automated feedback prototype system contributes to students incorporating more course concepts and concept relationships into their writing assignments, thus supports their learning of conceptual knowledge in a WTL activity

Providing Intelligent and Adaptive Support in Concept Map-based Learning Environments

abstract: Concept maps are commonly used knowledge visualization tools and have been shown to have a positive impact on learning. The main drawbacks of concept mapping are the requirement of training, and lack of feedback support. Thus, prior research has attempted to provide support and feedback in concept mapping, such as by developing computer-based concept mapping tools, offering starting templates and navigational supports, as well as providing automated feedback. Although these approaches have achieved promising results, there are still challenges that remain to be solved. For example, there is a need to create a concept mapping system that reduces the extraneous effort of editing a concept map while encouraging more cognitively beneficial behaviors. Also, there is little understanding of the cognitive process during concept mapping. What’s more, current feedback mechanisms in concept mapping only focus on the outcome of the map, instead of the learning process. This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps. Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping.Dissertation/ThesisDoctoral Dissertation Computer Science 201

概念マップと確信度情報に基づく適応的フィードバックに関する研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora