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

Abstract

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.

The effect on new knowledge and reviewed knowledge caused by the positioning task in closed concept maps

“…This approach has shown good results in retention when compared to traditional teaching, especially when the teacher uses the map to give the feedback (Sugihara et al 2012). This type of automatic diagnosis also allows for different automated feedback schemes, which has been effective for improving reading comprehension (Pailai et al 2018;Wu et al 2012).…”

“…This is the approach used for the dataset analyzed in this study. Other types of feedback have been used in past studies, such as asking the user to justify his incorrect propositions by using phrases of a text (Pailai et al 2018).…”

“…This allows for automated scoring of the maps by comparing the built map to an expert map. This mechanism also allows for automated feedback (Pailai et al 2018;Furtado et al 2018). Since closed concept maps provide information to the user, they can go beyond the reviewing activities that traditional concept maps provide.…”

“…Since closed concept maps provide information to the user, they can go beyond the reviewing activities that traditional concept maps provide. Closed concept maps with automated feedback can help students acquire new knowledge (Pailai et al 2018;Furtado et al 2018). While there is research on the reviewing aspects of concept maps (Cañas and Novak 2010;Armbruster and Anderson 1984;Van Dijk et al 1983), no research so far has investigated if the benefits of concept maps also transfer to the acquisition of new knowledge through automated feedback.…”

Open Information Structure Approach to Learning Support and Management with Kit-build Concept Map

“…To improve learner understanding in reading conditions, Pairai et al proposed adaptive feedback for the KB map with confidence tagging [22]. Learners can construct a concept map to express their understanding as a learner map, and identify confidence in each proposition of the learning map as an understanding level.…”