Supporting students' collective idea improvement through analytics-augmented metadiscourse in knowledge building

Abstract

This dissertation examines the effectiveness and roles of learning analytics-augmented metadiscourse in supporting collective idea improvement in a knowledge building learning environment. Metadiscourse is defined as collective metacognitive conversation that takes place in both knowledge forums and classrooms, during which students engage in reflective practices by identifying collective inquiry progress and making further inquiry plans. This study examines following research questions: (1) to what extent did learning analytics augmented meta-discourse support students’ conceptual learning gains and epistemic understanding of discourse; (2) how did students understand the effectiveness of the learning environment in supporting their inquiry process; (3) what characterise their engagement in Knowledge Building inquiry and what are differences in trajectories and participation patterns; (4) how did students engage with metadiscourse and to what extent does it support their knowledge-building inquiry. The study was conducted in a Singaporean secondary school science class and the participants were 22 8th grade students. The study lasted six weeks and was supported by two types of learning analytical tools, both of which were embedded in knowledge forum (i.e., word cloud and ideas building tool). The study was designed in four phases to examine the impact of different learning analytics and metadiscourse on students’ different types of knowledge building inquiries. Multiple types of data were collected, including tests on students’ conceptual understanding, open-ended questions, students’ knowledge forum (KF) discourse, artefacts including concept maps and group worksheets, and classroom discourse. Multiple types of analytical techniques were used, including thematic, inferential, social network analysis, and epistemic network analysis. Major findings of this study were as follows: (1) the learning analytics-augmented metadiscourse knowledge building (KB) sessions promote students’ understanding of photosynthesis and enriched their epistemic understanding of discourse; (2) across the four phases, students became more active participants; their ideas were more connected and students’ knowledge building discourse become more sophisticated. Results from different social network and epistemic network analyses indicate that students engaged in low-level discourse moves in the earlier phases and high-level in the later phases (i.e., summarizing major ideas, identifying future research directions and agency-driven discourse); (3) students were grouped as leader, regular and peripheral participants. Epistemic results showed that the leader group summarized and rise above major ideas, demonstrated highest posttest scores in conceptual understandings and conceptualized KB discourse as the emergence of new questions; (4) students engaged in metadiscourse reflecting on their knowledge building discourse on Knowledge Forum as identifying community idea gaps, creating rise-above understanding and extending KF inquiries. This dissertation has following implications. Theoretically, this study contributes to the existing literature on using learning analytics to support students’ metadiscourse. It also provides valuable insights into the theoretical relationships among important concepts in knowledge building, specifically, the study reveals students’ engagement in community metadiscourse is supported by analytics and artefacts. Methodologically, multiple types of network analysis approaches were combined to understand students’ KB inquiries across four phases to identify the trajectories of knowledge building journey. Pedagogically, the study provides valuable insights for educators to implement KB model and how to combine technological and pedagogical elements to the classroom knowledge building process.