Can online student performance be forecasted by learning analytics?

被引:6
|
作者
Strang, Kenneth David [1 ]
机构
[1] SUNY Coll Plattsburgh, Sch Business & Econ, Reg Higher Educ Ctr, 640 Bay Rd, Queensbury, NY 12804 USA
关键词
academic performance; big data analytics; ICT in higher education; Moodle engagement analytics; online undergraduate business course; student learning performance;
D O I
10.1504/IJTEL.2016.075950
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The paper focuses on utilising learning analytics to assess student performance in an online business. The value of this paper is in the literature review and the process used for analysis since it provides a way of how research in Moodle learning analytics or other similar systems could be leveraged to track student progress and for retention. The engagement analytics module is a relatively new component of Moodle but it has not yet been investigated with respect to student performance or in online business courses. The purpose of this study was to examine the predictive relationships between student academic performance and Moodle engagement analytics indicators along with student online activity data from the system logs. Unfortunately none of the hypothesised learning analytics factors were positively related to, nor could they predict, student academic performance. However, several interesting deductions from the learning analytics data gave rise to ideas for further research. Sense making of puzzling statistics suggested a mediating pattern of students' poor self-regulation skills because more focus was put on the assignment requirements but less on interacting with the lesson materials needed to complete the assignment and thereby resulting in lower grades.
引用
收藏
页码:26 / 47
页数:22
相关论文
共 50 条
  • [31] Designing an Online Discussion Strategy with Learning Analytics Feedback on the Level of Cognitive Presence and Student Interaction in an Online Learning Community
    Alwafi, Enas Mohammad
    ONLINE LEARNING, 2022, 26 (01): : 80 - 92
  • [32] Can student-facing analytics improve online students' effort and success by affecting how they explain the cause of past performance?
    Li, Qiujie
    Xu, Di
    Baker, Rachel
    Holton, Amanda
    Warschauer, Mark
    COMPUTERS & EDUCATION, 2022, 185
  • [33] Online Learning in Nutrition and Dietetics: Student Performance and Attitudes
    Upton, Dominic
    INTERNET JOURNAL OF ALLIED HEALTH SCIENCES AND PRACTICE, 2005, 3 (01):
  • [34] crsra: A Learning Analytics Tool for Understanding Student Behaviour in Massive Open Online Courses
    Hadavand, Aboozar
    Muschelli, John
    Leek, Jeffrey
    JOURNAL OF LEARNING ANALYTICS, 2019, 6 (02): : 140 - 152
  • [35] Learning Analytics of Student Participation and Achievement in Online Distance Education: A Structural Equation Modeling
    Koc, Mustafa
    EDUCATIONAL SCIENCES-THEORY & PRACTICE, 2017, 17 (06): : 1893 - 1910
  • [36] Relations between Student Online Learning Behavior and Academic Achievement in Higher Education: A Learning Analytics Approach
    Jo, Il-Hyun
    Yu, Taeho
    Lee, Hyeyun
    Kin, Yeonjoo
    EMERGING ISSUES IN SMART LEARNING, 2015, : 275 - 287
  • [37] Student Online Activity in Blended Learning: A Learning Analytics Perspective of Professional Teacher Education Studies in Finland
    Salonen, Arto O.
    Tapani, Annukka
    Suhonen, Sami
    SAGE OPEN, 2021, 11 (04):
  • [38] The impact of 151 learning designs on student satisfaction and performance: social learning (analytics) matters
    Rienties, Bart
    Toetenel, Lisette
    LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE,, 2016, : 339 - 343
  • [39] Learning analytics to monitor and predict student learning processes in problem solving activities during an online training
    Fissore, Cecilia
    Floris, Francesco
    Marchisio, Marina
    Rabellino, Sergio
    2023 IEEE 47TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC, 2023, : 481 - 489
  • [40] Using Learning Analytics to Identify Medical Student Misconceptions in an Online Virtual Patient Environment
    Poitras, Eric G.
    Naismith, Laura M.
    Doleck, Tenzin
    Lajoie, Susanne P.
    ONLINE LEARNING, 2016, 20 (02): : 183 - 194