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

22 items in this list.
Generated on Fri Nov 27 15:06:36 2020 GMT.

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2020To Top

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.

2019To Top

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.

2018To Top

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.

2017To Top

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.

2016To Top

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.

2015To Top

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.

2014To Top

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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