%0 Journal Article %1 Herodotou2019 %A Herodotou, Christothea %A Rienties, Bart %A Boroowa, Avinash %A Zdrahal, Zdenek %A Hlosta, Martin %D 2019 %J Educational Technology Research and Development %K highered implementation learninganalytics performance predictivemodeling retention students teaching %N 5 %P 1273--1306 %R 10.1007/s11423-019-09685-0 %T A large-scale implementation of predictive learning analytics in higher education: the teachers' role and perspective %U https://doi.org/10.1007/s11423-019-09685-0 %V 67 %X 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.

@article{Herodotou2019, abstract = {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.}, added-at = {2019-09-22T11:47:57.000+0200}, author = {Herodotou, Christothea and Rienties, Bart and Boroowa, Avinash and Zdrahal, Zdenek and Hlosta, Martin}, biburl = {https://www.bibsonomy.org/bibtex/232a6970c7c7d709c5b1c19778f515e6e/ereidt}, day = 01, doi = {10.1007/s11423-019-09685-0}, interhash = {26570d8eaffe44d296a109904dc78fdd}, intrahash = {32a6970c7c7d709c5b1c19778f515e6e}, issn = {1556-6501}, journal = {Educational Technology Research and Development}, keywords = {highered implementation learninganalytics performance predictivemodeling retention students teaching}, month = oct, number = 5, pages = {1273--1306}, timestamp = {2019-09-22T11:47:57.000+0200}, title = {A large-scale implementation of predictive learning analytics in higher education: the teachers' role and perspective}, url = {https://doi.org/10.1007/s11423-019-09685-0}, volume = 67, year = 2019 }