Investigating Differences in Wiki-based Collaborative Activities between Student Engagement Profiles in CS1

Abstract

Introductory computer science courses are being increasingly taught using technology-mediated instruction and e-learning environments. The software and technology in such courses could benefit from the use of student models to inform and guide customized support tailored to the needs of individual students. In this paper, we investigate how student motivated engagement profiles developed in educational research can be used as such models to predict student behaviors. These models are advantageous over those learned directly from observing individual students, as they rely on different data that can be available a priori before students use the technology. Using tracked behaviors of 249 students from 7 CS1 courses over the span of 3 semesters, we discover that students with different engagement profiles indeed behave differently in an online, wiki-based CSCL system while performing collaborative creative thinking exercises, and the differences between students are primarily as expected based on the differences in the profiles. Thus, such profiles could be useful as student models for providing customized support in e-learning environments in CS1 courses.

El trabajo colaborativo como estrategia didáctica para la enseñanza/aprendizaje de la programación: una revisión sistemática de literatura

“…[25], [27], [29], [33], [36], [37], [42], [45], [47]- [50], [52], [53], [56], [61] Celdas de aprendizaje 3 7 17,5 [26], [35], [40], [41], [44], [58], [60] Corrección por el compañero 1 3 7,5 [28], [46], [55] Resolución estructurada de problemas 3 3 7,5 [32], [38], [59] Juego …”

“…Se presenta un CSCL -Computer-Supported Collaborative Learning en línea basado en Wiki [50], enfocado a resolver ejercicios de programación, que, dada su filosofía doblemente colaborativa, permite a los estudiantes realizar tres acciones clave: ver, editar y comentar las páginas de wiki.…”

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

“…Scholars have examined how students feel, behave, and think when engaged in AOD and how those affective, behavioral, and cognitive dimensions interact with each other and influence achievement in online learning (Kim and Ayer 2019;Eck et al 2016;Gallego-Arrufat et al 2015;Järvenoja et al 2017;Kim et al 2018;Marchand and Gutierrez 2012;Tempelaar et al 2012;Wise and Hsiao 2018;Xie et al 2019). For example, student emotions significantly explained student behavior in an online learning community (Eck et al 2016), sequential causal paths emerged from motivation to emotion to learner behaviors (Marchand and Gutierrez 2012;Tempelaar et al 2012), and student self-regulatory behaviors (e.g., planning, monitoring, evaluating, and controlling one's learning-related behaviors) predicted the degree of cognitive engagement (Author 2020; Kim et al 2018;Wise and Hsiao 2018;Xie et al 2019).…”