A longitudinal examination of temporal and iterative relationships among learner engagement dimensions during online discussion

Abstract

Scholars have examined how students feel, behave, and think when engaged in asynchronous online discussion (AOD), which has become a prevalent means of collaborative learning in higher education. However, few have considered the impact of time on these learning-related dimensions, partly due to reliance on self-reported instruments that are not sufficient to measure the temporal, changeable, and iterative relationships among learning-related dimensions during online conversation. The purpose of the current study was to use learning analytics to investigate longitudinally the relationships among learning-related emotions, learner participation level, and cognitive effort of students. Analysis of system-stored student data from 56 students enrolled in a wiki-based, blended undergraduate writing course revealed that positive or negative expressed emotions led to higher participation in ongoing discussions and that participation level predicted the rate of change in textual revision. These findings not only confirm theories of learner engagement but also demonstrate the viability of trace data and their potential in educational research and practice.

Does learning happen? A mixed study of online chat data as an indicator of student participation in an online English course

“…In fact, previous literature has found that conveying feelings and emotions, either positive or negative, could lead to higher participation. For example, engaging and productive comments can evoke joyful feelings which in turn can drive further participation in ongoing communication, conversation, discussion or other forms of learning activities that follow (Kim et al, 2021 ).…”

“…Among them, studies that have examined students’ real-time chat data in the chat box of online learning medium are scant. In fact, real-time chat data from the chat box of online learning medium is able to capture the dynamic nature of learner participation and might reflect the real time conditions of learning and deliver the up-to-the-minute information (Kim et al, 2021 ). In online courses, students tend to spontaneously type in all kinds of text messages in the chat box on the online learning mediums such as ZOOM Meeting or Tencent Meeting.…”

A motivação dos estudantes em ambientes computacionais de aprendizagem: uma revisão sistemática da literatura

“…São caracterizadas pelo conjunto de ferramentas que permite produção e administração de cursos ou atividades (de Oliveira, Nakayama, Torres, & Nunes, 2020), o que pode facilitar a mensuração de dados definidos nos artigos como motivação. Os artigos selecionados que utilizam esse recurso de aprendizagem são (Shen, RBIE v.32 -2024 Callaghan, & Shen, 2008;Nasoz & Bayburt, 2009;Rodrigues, Fdez-Riverola, & Novais, 2011;Grafsgaard, Wiggins, Boyer, Wiebe, & Lester, 2013;Gutiérrez y Restrepo et al, 2016;Artífice, Ferreira, Marcelino-Jesus, Sarraipa, & Jardim-Gonçalves, 2017;Pelánek, Papoušek, Řihák, Stanislav, & Nižnan, 2017;Dias, Hadjileontiadou, Diniz, & Hadjileontiadis, 2019; M. K. Kim & Ketenci, 2020;Iulamanova, Bogdanova, & Kotelnikov, 2021; M. K. Kim, Lee, & Kim, 2021).…”

“…Em relação aos artigos selecionados, este recurso é utilizado em (Katsionis & Virvou, 2008;Rodrigues et al, 2011;Girard et al, 2013;Grafsgaard et al, 2013;Arroyo et al, 2014;M. K. Kim & Ketenci, 2020;M. K. Kim et al, 2021;Altuwairqi et al, 2021).…”

Investigating relationships among regulated learning, teaching presence and student engagement in blended learning: An experience sampling analysis

“…It is possible that engagement indicators function differently at the activity and course levels: Confusion noted in real time might be an indicator of focused engagement, but confusion recalled later (e.g., in an end-of-course survey) might indicate residual frustration and anxiety. 2) Using general-level measurements, it is difficult to demonstrate changes in learner engagement over time and explore the combined effects of individual and contextual factors that contribute to such changes (Archambault & Dupéré, 2017;Kim et al, 2020;Saqr & López-Pernas, 2021). Literature suggests that self-regulated learning (SRL) is a crucial individual factor (Ali & Hanna, 2021;Anthonysamy et al, 2020;Han et al, 2022), as well as teaching presence being a crucial contextual factor influencing learner engagement (Zhong et al, 2022;Wang, 2022).…”