Unpacking student engagement in higher education learning analytics: a systematic review

被引:1
作者
Bergdahl, Nina [1 ,2 ]
Bond, Melissa [3 ,4 ,5 ]
Sjoeberg, Jeanette [1 ]
Dougherty, Mark [1 ]
Oxley, Emily [6 ]
机构
[1] Halmstad Univ, Halmstad, Sweden
[2] Stockholm Univ, Stockholm, Sweden
[3] UCL, EPPI Ctr, London, England
[4] Univ Stavanger, Stavanger, Norway
[5] Natl Inst Teaching, London, England
[6] Univ Glasgow, Sch Social & Environm Sustainabil, Glasgow, Scotland
来源
INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION | 2024年 / 21卷 / 01期
关键词
ONLINE; ENVIRONMENTS; BEHAVIOR; OUTCOMES; IMPACT; CLASSROOM; SCIENCE; DESIGN; MODEL;
D O I
10.1186/s41239-024-00493-y
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Educational outcomes are heavily reliant on student engagement, yet this concept is complex and subject to diverse interpretations. The intricacy of the issue arises from the broad spectrum of interpretations, each contributing to the understanding of student engagement as both complex and multifaceted. Given the emergence and increasing use of Learning Analytics (LA) within higher education to provide enhanced insight into engagement, research is needed to understand how engagement is conceptualised by LA researchers and what dimensions and indicators of engagement are captured by studies that use log data. This systematic review synthesises primary research indexed in the Web of Science, Scopus, ProQuest, A + Education, and SAGE journals or captured through snowballing in OpenAlex. Studies were included if they were published between 2011 and 2023, were journal articles or conference papers and explicitly focused on LA and engagement or disengagement within formal higher education settings. 159 studies were included for data extraction within EPPI Reviewer. The findings reveal that LA research overwhelmingly approaches engagement using observable behavioural engagement measures, such as clicks and task duration, with very few studies exploring multiple dimensions of engagement. Ongoing issues with methodological reporting quality were identified, including a lack of detailed contextual information, and recommendations for future research and practice are provided.
引用
收藏
页数:33
相关论文
共 158 条
[1]   Complementing Educational Recommender Systems with Open Learner Models [J].
Abdi, Solmaz ;
Khosravi, Hassan ;
Sadiq, Shazia ;
Gasevic, Dragan .
LAK20: THE TENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, 2020, :360-365
[2]   Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models [J].
Adnan, Muhammad ;
Habib, Asad ;
Ashraf, Jawad ;
Mussadiq, Shafaq ;
Raza, Arsalan Ali ;
Abid, Muhammad ;
Bashir, Maryam ;
Khan, Sana Ullah .
IEEE ACCESS, 2021, 9 :7519-7539
[3]   Impact of E-Learning Orientation, Moodle Usage, and Learning Planning on Learning Outcomes in On-Demand Lectures [J].
Aida, Saori .
EDUCATION SCIENCES, 2023, 13 (10)
[4]   Investigating the impact of a gamified learning analytics dashboard: Student experiences and academic achievement [J].
Alam, Md. I. ;
Malone, Lauren ;
Nadolny, Larysa ;
Brown, Michael ;
Cervato, Cinzia .
JOURNAL OF COMPUTER ASSISTED LEARNING, 2023, 39 (05) :1436-1449
[5]   Online Environments for Supporting Learning Analytics in the Flipped Classroom: A Scoping Review [J].
Algayres, Muriel ;
Triantafyllou, Evangelia .
PROCEEDINGS OF THE 18TH EUROPEAN CONFERENCE ON E-LEARNING (ECEL 2019), 2019, :16-23
[6]   Investigating Boredom and Engagement during Writing Using Multiple Sources of Information: The Essay, The Writer, and Keystrokes [J].
Allen, Laura K. ;
Mills, Caitlin ;
Jacovina, Matthew E. ;
Crossley, Scott ;
D'Mello, Sidney ;
McNamara, Danielle S. .
LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE,, 2016, :114-123
[7]   What to Blend? Exploring the Relationship Between Student Engagement and Academic Achievement via a Blended Learning Approach [J].
Argyriou, Paraskevi ;
Benamar, Kenza ;
Nikolajeva, Milena .
PSYCHOLOGY LEARNING AND TEACHING-PLAT, 2022, 21 (02) :126-137
[8]   Targeting At-risk Students Using Engagement and Effort Predictors in an Introductory Computer Programming Course [J].
Azcona, David ;
Smeaton, Alan F. .
DATA DRIVEN APPROACHES IN DIGITAL EDUCATION, 2017, 10474 :361-366
[9]   SELF-EFFICACY - TOWARD A UNIFYING THEORY OF BEHAVIORAL CHANGE [J].
BANDURA, A .
PSYCHOLOGICAL REVIEW, 1977, 84 (02) :191-215
[10]  
Banihashem S. K., 2020, doctoral dissertation