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In the learner model, acknowledging the affective aspects of learning is important for a range of learner outcomes, including motivation, persistence, and engagement. Learners\u2019 affective states can be detected using physical (e.g. cameras) and physiological sensors (e.g., EEG) in online learning. Although these detectors demonstrate high accuracy, they raise privacy concerns for learners and present challenges in deploying them on a large scale to larger groups of students or in classroom settings. Consequently, researchers have designed an alternative method that can recognise students\u2019 affective states at any point during online learning from their interaction with a computer-based learning platform (i.e. intelligent tutoring systems) without using any sensors. Existing sensor-free affect detectors however, are less accurate and not directly generalisable to other domains and systems. This research focuses on developing generalisable sensor-free affect detectors to identify students\u2019 frustration during online learning using machine learning classifiers. The detectors were built by identifying minimal optimal features associated with frustration from the high-dimensional feature space through a series of experiments on a real-world students\u2019 affective dataset, which are generalisable across various learning platforms and domains. To evaluate their accuracy and generalisability, the detectors\u2019 performance was validated on two independent datasets collected from different educational institutions. The experimental results show that cost-sensitive Bayesian classifiers can achieve higher affect detection accuracies with a small number of generalisable features compared to other classifiers.<\/jats:p>","DOI":"10.1007\/s11257-024-09402-4","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T11:02:42Z","timestamp":1719226962000},"page":"1493-1527","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Generalisable sensor-free frustration detection in online learning environments using machine learning"],"prefix":"10.1007","volume":"34","author":[{"given":"Mohammad Mustaneer","family":"Rahman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Ollington","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soonja","family":"Yeom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadia","family":"Ollington","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"9402_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2017.2714671","author":"SM Alarc\u00e3o","year":"2019","unstructured":"Alarc\u00e3o, S.M., Fonseca, M.J.: Emotions recognition using EEG signals: a survey. 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