Με τη χρηματοδότηση της Ευρωπαϊκής Ένωσης. Οι απόψεις και οι γνώμες που διατυπώνονται εκφράζουν αποκλειστικά τις απόψεις των συντακτών και δεν αντιπροσωπεύουν κατ'ανάγκη τις απόψεις της Ευρωπαϊκής Ένωσης ή του Ευρωπαϊκού Εκτελεστικού Οργανισμού Εκπαίδευσης και Πολιτισμού (EACEA). Η Ευρωπαϊκή Ένωση και ο EACEA δεν μπορούν να θεωρηθούν υπεύθυνοι για τις εκφραζόμενες απόψεις. - Qualitative analysis of student reports for insights on AIC benefits and drawbacks. Lessons learned AIC potential: - Can offer immediate feedback and a supportive learning environment. - Need improvement in speech technologies (recognition and synthesis). - Must better adapt to different learner proficiency levels. Design significance: User-friendly interfaces, multimedia, and the potential of emerging tech (AR/VR) are crucial for engagement. CHISM's value: The model provides a comprehensive framework for AIC assessment within language learning contexts. Implications for practice Personalisation is key: AICs need to tailor experiences more effectively to sustain learner interest. Μελέτη περίπτωσης 34: Εμπειρίες από τη χρήση ενός προσαρμοστικού ευφυούς συστήματος για τη βελτίωση των επιδόσεων των εκπαιδευομένων σε διαδικτυακά περιβάλλοντα: μια μελέτη περίπτωσης σε μαθήματα οικονομικών και διοίκησης επιχειρήσεων. General information Reference/Source: Guerrero-Roldán, A. E., Rodríguez-González, M. E., Bañeres, D., ElasriEjjaberi, A., & Cortadas, P. (2021). Experiences in the use of an adaptive intelligent system to enhance online learners’ performance: a case study in Economics and Business courses. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00271-0 Institution: Universitat Oberta de Catalunya, Spain (fully online, public institution). Course/subject: Two first-year courses in the BSc of Economics and Business program: Introduction to Enterprise and Markets and Behavior Aim: Researchers are developing an Early Warning System (EWS) called the Learning Intelligent System (LIS) to address the issue of student dropout and improve overall performance in online courses. Target group: Undergraduate students enrolled in the specified courses. Description of case Overview: - Problem: Universities, especially ones that are fully online, often see students struggle and drop out of courses. This can happen for many reasons – maybe the student is new and overwhelmed, or the workload is unexpectedly heavy. - Possible solution: The Universitat Oberta de Catalunya in Spain is developing a special computer system called the Learning Intelligent System (LIS) to address this problem. The LIS is designed to help students stay on track and succeed in their courses. - How LIS Works: - It watches how students are doing throughout the course. - It uses past student data to predict if someone might be struggling. - It shows students and teachers how things are going with a simple "traffic light" system. - It sends personalised messages to students who may need some extra help. Research: - Researchers tested the LIS in two economics courses over two semesters. - Researchers compared students' grades and how many dropped out when the LIS was used to a semester when it wasn't. - Researchers also asked students what they thought of the LIS system in a survey. Lessons learned Performance impact:
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