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

Bayer, Vaclav; Mulholland, Paul; Hlosta, Martin; Farrell, Tracie; Herodotou, Christothea and Fernandez, Miriam (2024). Co‐creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education. British Journal of Educational Technology (Early access).

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Bonnin, Geoffray; Bayer, Vaclav; Fernandez, Miriam; Herodotou, Christothea; Hlosta, Martin and Mulholland, Paul (2023). CERSEI: Cognitive Effort Based Recommender System for Enhancing Inclusiveness. In: Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200, Lecture Notes in Computer Science, Springer, Cham, pp. 692–697.

Herodotou, Christothea; Maguire, Claire; Hlosta, Martin and Mulholland, Paul (2023). Predictive Learning Analytics and University Teachers: Usage and perceptions three years post implementation. In: LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference, pp. 68–78.

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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.

Hlosta, Martin; Herodotou, Christothea; Papathoma, Tina; Gillespie, Anna and Bergamin, Per (2022). Predictive learning analytics in online education: A deeper understanding through explaining algorithmic errors. Computers and Education: Artificial Intelligence, 3, article no. 100108.

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Herodotou, Christothea; Maguire, Claire; McDowell, Nicola D.; Hlosta, Martin and Boroowa, Avinash (2021). The engagement of university teachers with predictive learning analytics. Computers & Education, 173, article no. 104285.

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Bayer, Vaclav; Hlosta, Martin and Fernandez, Miriam (2021). Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally? In: Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12749 (Roll, I.; McNamara, D.; Sosnovsky, S.; Luckin, R. and Dimitrova, V. eds.), Springer.

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Hlosta, Martin; Herodotou, Christothea; Fernandez, Miriam and Bayer, Vaclav (2021). Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, the Netherlands, June 14-18, 2021, Proceedings, Part II (Roll, Ido; McNamara, Danielle; Sosnovsky, Sergey; Luckin, Rose and Dimitrova, Vania eds.), Lecture Notes in Artificial Intelligence, Springer, Cham, pp. 190–195.

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Rets, Irina; Herodotou, Christothea; Bayer, Vaclav; Hlosta, Martin and Rienties, Bart (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education, 18(1), article no. 46.

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Herodotou, Christothea; Rienties, Bart; Hlosta, Martin; Boroowa, Avinash; Mangafa, Chrysoula and Zdrahal, Zdenek (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. Internet and Higher Education, 45, article no. 100725.

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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.

Herodotou, Christothea; Boroowa, Avinash; Hlosta, Martin and Rienties, Bart (2020). What do distance learning students seek from student analytics? In: International Conference on Learning Sciences, 19-23 Jun 2020, Nashville, TN, USA.

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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.

Herodotou, Christothea; Hlosta, Martin; Boroowa, Avinash; Rienties, Bart; Zdrahal, Zdenek and Mangafa, Chrysoula (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 50(6) pp. 3064–3079.

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Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek and Hlosta, Martin (2019). A large-scale implementation of Predictive Learning Analytics in Higher Education: the teachers' role and perspective. Educational Technology Research and Development, 67(5) pp. 1273–1306.

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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).

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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.

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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.

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Hlosta, Martin; Zdrahal, Zdenek and Zendulka, Jaroslav (2018). Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data. Knowledge-Based Systems, 160 pp. 278–295.

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Kuzilek, Jakub; Hlosta, Martin and Zdrahal, Zdenek (2017). Open University Learning Analytics dataset. Scientific Data, 4, article no. 170171.

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Rienties, Bart; Clow, Doug; Coughlan, Tim; Cross, Simon; Edwards, Chris; Gaved, Mark; Herodotou, Christothea; Hlosta, Martin; Jones, Jan; Rogaten, Jekaterina and Ullmann, Thomas (2017). Scholarly insight Autumn 2017:a Data wrangler perspective. Open University UK.

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Herodotou, Christothea; Gilmour, Alison; Boroowa, Avinash; Rienties, Bart; Zdrahal, Zdenek and Hlosta, Martin (2017). Predictive modelling for addressing students’ attrition in Higher Education: The case of OU Analyse. In: CALRG Annual Conference 2017, 14-16 Jun 2017, The Open University, Milton Keynes, UK.

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.

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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.

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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.

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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.

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Kuzilek, Jakub; Hlosta, Martin; Herrmannova, Drahomira; Zdrahal, Zdenek; Vaclavek, Jonas and Wolff, Annika (2015). OU Analyse: analysing at-risk students at The Open University. Learning Analytics Review, LAK15-1 pp. 1–16.

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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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