A large-scale implementation of predictive learning analytics in higher education: the teacher's role and perspective

By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data,...

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Published inEducational technology research and development Vol. 67; no. 5; pp. 1273 - 1306
Main Authors Herodotou, Christothea, Rienties, Boroowa, Avinash, Zdrahal, Zdenek, Hlosta, Martin
Format Journal Article
LanguageEnglish
Published New York Springer 01.10.2019
Springer US
Springer Nature B.V
Subjects
Academic Achievement
Analysis
At Risk Students
College teachers
Data Use
DEVELOPMENT ARTICLE
Distance Education
Distance learning
Education
Education parks
Education, Higher
Educational Technology
Equipment and supplies
Higher Education
Identification
Intervention
Learning Analytics
Learning and Instruction
Prediction
School facilities
Teacher Attitudes
Teacher Empowerment
Teacher Role
Teachers
Teaching
Teaching Methods
Student performance
Teachers
Higher education
Retention
Predictive learning analytics
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Summary:By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction. The aim of this study is to evaluate whether providing teachers in a distance learning higher education institution with PLA data predicts students' performance and empowers teachers to identify and assist students at risk. Using principles of Technology Acceptance and Academic Resistance models, a university-wide, multi-methods study with 59 teachers, nine courses, and 1325 students revealed that teachers can positively affect students' performance when engaged with PLA. Follow-up semi-structured interviews illuminated teachers' actual uses of the predictive data and revealed its impact on teaching practices and intervention strategies to support students at risk.
ISSN:1042-1629
1556-6501
DOI:10.1007/s11423-019-09685-0