Currently browsing: Items authored or edited by Martin Hlosta https://orcid.org/0000-0002-7053-7052

27 items in this list.
Generated on Thu Jan 20 02:43:05 2022 GMT.

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Bonnin, Geoffray; Dessì, Danilo; Fenu, Gianni; Hlosta, Martin; Marras, Mirko and Sack, Harald (2022). Guest Editorial of the FGCS Special Issue on Advances in Intelligent Systems for Online Education. Future Generation Computer Systems, 127 pp. 331–333.

Conference or Workshop ItemTo Top

Bayer, Vaclav; Hlosta, Martin and Fernandez, Miriam (2021). Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally? In: AIED 2021; 22nd International Conference on Artificial Intelligence in Education, 14-18 Jun 2021, ONLINE from Utrecht.

Hlosta, Martin; Zdrahal, Zdenek; Bayer, Vaclav and Herodotou, Christothea (2020). Why Predictions of At-Risk Students Are Not 100% Accurate? Showing Patterns in False Positive and False Negative Predictions. In: Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 23-27 Mar 2020, Frankfurt am Main, Germany.

Hlosta, Martin; Bayer, Vaclav and Zdrahal, Zdenek (2020). Mini Survival Kit: Prediction based recommender to help students escape their critical situation in online courses. In: Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 23-27 Mar 2020, Frankfurt am Main, Germany.

Hlosta, Martin; Papathoma, Tina and Herodotou, Christothea (2020). Explaining Errors in Predictions of At-Risk Students in Distance Learning Education. In: Artificial Intelligence in Education, Lecture Notes in Computer Science (LNCS), Springer, pp. 119–123.

Hlosta, Martin; Kocvara, Jakub; Beran, David and Zdrahal, Zdenek (2019). Visualisation of key splitting milestones to support interventions. In: Companion Proceedings 9th International Conference on Learning Analytics & Knowledge (LAK19).

Celik, Dilek; Mikroyannidis, Alexander; Hlosta, Martin; Third, Allan and Domingue, John (2019). ADA: A System for Automating the Learning Data Analytics Processing Life Cycle. In: EC-TEL 2019 14th European Conference on Technology Enhanced Learning, 16-19 Sep 2019, Delft, Netherlands, pp. 714–718.

Huptych, Michal; Hlosta, Martin; Zdrahal, Zdenek and Kocvara, Jakub (2018). Investigating Influence of Demographic Factors on Study Recommenders. In: Artificial Intelligence in Education (Rosé, Carolyn Penstein; Martínez-Maldonado, Roberto; Hoppe, H. Ulrich; Luckin, Rose; Mavrikis, Manolis; Porayska-Pomsta, Kaska; McLaren, Bruce and du Boulay, Benedict eds.), Lecture Notes in Artificial Intelligence, Springer, Cham, pp. 150–154.

Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek; Hlosta, Martin and Naydenova, Galina (2017). Implementing predictive learning analytics on a large scale: the teacher's perspective. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, ACM, NY, pp. 267–271.

Huptych, Michal; Bohuslavek, Michal; Hlosta, Martin and Zdrahal, Zdenek (2017). Measures for recommendations based on past students' activity. In: LAK '17 Proceedings of the Seventh International Learning Analytics & Knowledge Conference on - LAK '17, pp. 404–408.

Hlosta, Martin; Zdrahal, Zdenek and Zendulka, Jaroslav (2017). Ouroboros: early identification of at-risk students without models based on legacy data. In: LAK17 - Seventh International Learning Analytics & Knowledge Conference, 13-17 Mar 2017, Vancouver, BC, Canada, pp. 6–15.

Zdrahal, Zdenek; Hlosta, Martin and Kuzilek, Jakub (2016). Analysing performance of first year engineering students. In: Learning Analytics and Knowledge: Data literacy for Learning Analytics Workshop, 26 Apr 2016, Edinburgh.

Herrmannova, Drahomira; Hlosta, Martin; Kuzilek, Jakub and Zdrahal, Zdenek (2015). Evaluating Weekly Predictions of At-Risk Students at The Open University: Results and Issues. In: EDEN 2015 Annual Conference Expanding Learning Scenarios: Opening Out the Educational Landscape, 9-12 Jun 2015, Barcelona, Spain.

Wolff, Annika; Zdrahal, Zdenek; Herrmannova, Drahomira; Kuzilek, Jakub and Hlosta, Martin (2014). Developing predictive models for early detection of at-risk students on distance learning modules. In: Machine Learning and Learning Analytics Workshop at The 4th International Conference on Learning Analytics and Knowledge (LAK14), 24-28 Mar 2014, Indianapolis, Indiana, USA.

Hlosta, Martin; Herrmannova, Drahomira; Vachova, Lucie; Kuzilek, Jakub; Zdrahal, Zdenek and Wolff, Annika (2014). Modelling student online behaviour in a virtual learning environment. In: Machine Learning and Learning Analytics workshop at The 4th International Conference on Learning Analytics and Knowledge (LAK14), 24-28 March 2014, Indianapolis, Indiana, USA, 24-28 Mar 2014, Indianapolis, Indiana, USA.

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