Items where Author is "Smeaton, Alan F."

Article

Dey-Plissonneau, Aparajita, Lee, Hyowon, Scriney, Michael, Smeaton, Alan F., Pradier, Vincent and Riaz, Hamza (2021) Facilitating reflection in teletandem through automatically generated conversation metrics and playback video.

Lee, Hyowon, Liu, Mingming, Scriney, Michael and Smeaton, Alan F. (2021) sage-based summaries of learning videos.

Lee, Hyowon, Liu, Mingming, Riaz, Hamza, Rajasekaran, Navaneethan, Scriney, Michael and Smeaton, Alan F. (2021) Attention based video summaries of live online zoom classes.

Lee, Hyowon, Scriney, Michael, Dey-Plissonneau, Aparajita and Smeaton, Alan F. (2021) Supporting an effective review of telecollaboration for second language learning by visualising the participation and engagement at Dublin City University.

Azcona, David, Hsiao, I-Han and Smeaton, Alan F. (2019) Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction, 29 (4). pp. 759-788. ISSN 0924-1868

Azcona, David, Arora, Piyush, Hsiao, I.-Han and Smeaton, Alan F. (2019) user2code2vec: embeddings for profiling students based on distributional representations of source code.

Azcona, David, Hsiao, I.-Han and Smeaton, Alan F. (2018) Personalizing computer science education by leveraging multimodal learning analytics.

Azcona, David, Hsiao, I.-Han and Smeaton, Alan F. (2018) PredictCS: Personalizing Programming learning by leveraging learning analytics.

Corrigan, Owen and Smeaton, Alan F. (2017) A course agnostic approach to predicting student success from VLE log data using recurrent neural networks.

Azcona, David and Smeaton, Alan F. (2017) Targeting at-risk students using engagement and effort predictors in an introductory computer programming course.

Azcona, David, Corrigan, Owen, Scanlon, Philip and Smeaton, Alan F. (2017) Innovative learning analytics research at a data-driven HEI.

Scanlon, Philip and Smeaton, Alan F. (2016) Identifying the impact of friends on their peers academic performance.

Scanlon, Philip and Smeaton, Alan F. (2016) Using data analytics to predict peer-group effects on student exam results.

Corrigan, Owen, Glynn, Mark, McKenna, Aisling, Smeaton, Alan F. and Smyth, Sinéad (2015) Student data: data is knowledge – putting the knowledge back in the students’ hands.

Corrigan, Owen, Smeaton, Alan F., Glynn, Mark and Smyth, Sinéad (2015) Using educational analytics to improve test performance.

Corrigan, Owen, Glynn, Mark, Smeaton, Alan F. and Smyth, Sinéad (2015) Mining access logs with predictive analytics to improve student performance.