Journal Article

Explanatory inferencing in simulation-based discovery learning: sequence analysis using the edit distance median string

Mahmoody Ghaidary, Ahmad, Obaid, Teeba, Nesbit, John C, Jain, Misha, Hajian, Shiva

2023

Overview

Understanding scientific phenomena requires learners to construct mental models of causal systems. Simulation-based discovery learning offers learners the opportunity to construct mental models and test them against the behavior of a simulation. The purpose of this study was to investigate sequential patterns of learner actions and utterances associated with outcomes of simulation-based guided discovery learning. We conducted a sequence analysis of data gathered from 11 undergraduate students engaged in discovery learning. Three related methods were used for the sequence analysis: Levenshtein edit distance, k-means clustering of the Levenshtein distance, and the Kohonen generalized median sequence. The median sequences of high-gaining and low-gaining participants showed qualitative differences in how they gathered evidence, stated claims, and drew explanatory inferences. Differences between the sequences of actions and utterances of high-gaining and low-gaining participants suggested ways that students might be guided to enhance discovery learning. By tracking the learning patterns of learners, researchers can determine the conditions under which prompts should be provided and offer recommendations for transforming less effective learning strategies to more effective ones.

• Publisher: Springer Nature B.V
• Subject: Causal models / Clustering / College students / Discovery / Discovery learning / Learning / Learning strategies / Mental models / Sequences / Simulation / Tracking / Undergraduate students / Utterances