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- Abdelhai, Rehab, et al.
(författare)
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An e-learning reproductive health module to support improved student learning and interaction : a prospective interventional study at a medical school in Egypt
- 2012
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Ingår i: BMC Medical Education. - : Springer Science and Business Media LLC. - 1472-6920. ; 12, s. 11-
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Tidskriftsartikel (refereegranskat)abstract
- Background: The Public Health (PH) course at the medical college of Cairo University is based on traditional lectures. Large enrollment limits students' discussions and interactions with instructors. Aim: Evaluate students' learning outcomes as measured by improved knowledge acquisition and opinions of redesigning the Reproductive Health (RH) section of the PH course into e-learning and assessing e-course utilization. Methods: This prospective interventional study started with development of an e-learning course covering the RH section, with visual and interactive emphasis, to satisfy students' diverse learning styles. Two student groups participated in this study. The first group received traditional lecturing, while the second volunteered to enroll in the e-learning course, taking online course quizzes. Both groups answered knowledge and course evaluation questionnaires and were invited to group discussions. Additionally, the first group answered another questionnaire about reasons for non-participation. Results: Students participating in the e-learning course showed significantly better results, than those receiving traditional tutoring. Students who originally shunned the e-course expressed eagerness to access the course before the end of the academic year. Overall, students using the redesigned e-course reported better learning experiences. Conclusions: An online course with interactivities and interaction, can overcome many educational drawbacks of large enrolment classes, enhance student's learning and complement pit-falls of large enrollment traditional tutoring.
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- Afzaal, Muhammad, et al.
(författare)
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Automatic and Intelligent Recommendations to Support Students’ Self-Regulation
- 2021
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Ingår i: International Conference on Advanced Learning Technologies (ICALT). - 9781665441063 ; , s. 336-338
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student's self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students' performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student's performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.
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- Afzaal, Muhammad, et al.
(författare)
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Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-Regulation
- 2021
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Ingår i: Frontiers in Artificial Intelligence. - : Frontiers Media SA. - 2624-8212. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Formative feedback has long been recognised as an effective tool for student learning, and researchers have investigated the subject for decades. However, the actual implementation of formative feedback practices is associated with significant challenges because it is highly time-consuming for teachers to analyse students’ behaviours and to formulate and deliver effective feedback and action recommendations to support students’ regulation of learning. This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent feedback and action recommendations that support student’s self-regulation in a data-driven manner, aiming to improve their performance in courses. Prior studies within the field of learning analytics have predicted students’ performance and have used the prediction status as feedback without explaining the reasons behind the prediction. Our proposed method, which has been developed based on LMS data from a university course, extends this approach by explaining the root causes of the predictions and by automatically providing data-driven intelligent recommendations for action. Based on the proposed explainable machine learning-based approach, a dashboard that provides data-driven feedback and intelligent course action recommendations to students is developed, tested and evaluated. Based on such an evaluation, we identify and discuss the utility and limitations of the developed dashboard. According to the findings of the conducted evaluation, the dashboard improved students’ learning outcomes, assisted them in self-regulation and had a positive effect on their motivation.
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- Afzaal, Muhammad, et al.
(författare)
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Generation of Automatic Data-Driven Feedback to Students Using Explainable Machine Learning
- 2021
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Ingår i: Artificial Intelligence in Education. - Cham : Springer. - 9783030782702 ; , s. 37-42
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel approach that employs learning analytics techniques combined with explainable machine learning to provide automatic and intelligent actionable feedback that supports students self-regulation of learning in a data-driven manner. Prior studies within the field of learning analytics predict students’ performance and use the prediction status as feedback without explaining the reasons behind the prediction. Our proposed method, which has been developed based on LMS data from a university course, extends this approach by explaining the root causes of the predictions and automatically provides data-driven recommendations for action. The underlying predictive model effectiveness of the proposed approach is evaluated, with the results demonstrating 90 per cent accuracy.
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- Afzaal, Muhammad, et al.
(författare)
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Informative Feedback and Explainable AI-Based Recommendations to Support Students' Self-regulation
- 2024
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Ingår i: Technology, Knowledge and Learning. - 2211-1662 .- 2211-1670. ; 29:1, s. 331-354
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Tidskriftsartikel (refereegranskat)abstract
- Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students' learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students' performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students' academic performance. The results revealed that the dashboard significantly enhanced students' learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students' performance and assisted them in self-regulation
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- Ahmad, Fouad, et al.
(författare)
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An e-Learning Faculty Development Course with Formative Assessment
- 2010
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Ingår i: 16th Annual Sloan Consortium International Conference on Online Learning.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- An e-Learning faculty development course has been developed and taught in Egypt for the past five years. The course adopts Formative Assessment (FA) techniques to enhance participants' learning. The additional FA effort for collecting and analyzing data as well as adjusting teaching is worth investing to accommodate gaps in learning.
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- Arborelius, Lotta, et al.
(författare)
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A new interactive computer simulation system for violence risk assessment of mentally disordered violent offenders
- 2013
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Ingår i: CBMH. Criminal behaviour and mental health. - : Wiley. - 0957-9664 .- 1471-2857. ; 23:1, s. 30-40
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Tidskriftsartikel (refereegranskat)abstract
- Background Assessment of risk of future violence has developed from reliance on static indicators towards a more dynamic approach. In the latter context, however, the offender is seldom confronted with real life situations. Aims The aim of this study is to evaluate a computer-based system Reactions on Display, which presents human interactions based on real-life situations for its effectiveness in distinguishing between potentially violent offenders with mental disorder and a healthy comparison group. Methods Male offenders with autism spectrum disorders or psychosis were recruited from specialist forensic psychiatric units in Sweden and healthy participants from the local communities. Each consenting participant was presented with film clips of a man in neutral and violent situations, which at critical moments stopped the story to ask him to predict the thoughts, feelings and actions of the actor. Results Offender patients, irrespective of diagnosis, detected fewer emotional reactions in the actor in the non-violent sequence compared with controls. When asked to choose one of four violent actions, the offender patients chose more violent actions than did the controls. They also reported fewer physical reactions in the actors when actors were being violent. There were also some examples of incongruent or deviant responses by some individual patients. Conclusions and implications for practice The use of interactive computer simulation techniques is not only generally acceptable to offender patients, but it also helps to differentiate their current response style to particular circumstances from that of healthy controls in a way that does not rely on their verbal abilities and may tap more effectively into their emotional reactions than standard verbal questions and answer approaches. This may pave the way for Reactions on Display providing a useful complement to traditional risk assessment, and a training route with respect to learning more empathic responding, thus having a role in aiding risk management.
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- Bahati, Bernard, et al.
(författare)
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Can Student Engagement in Online Courses Predict Performance on Online Knowledge Surveys?
- 2017
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Ingår i: International Journal of Learning, Teaching and Educational Research. - 1694-2493 .- 1694-2116. ; 16:3, s. 73-87
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Tidskriftsartikel (refereegranskat)abstract
- The link between student engagement and academic performance has been widely examined. However, most of these studies have focused on ascertaining the existence of such a relationship on the summative assessment level. By comparing students’ experience points in an online course and students’ scores on online knowledge surveys (KS), this study examined the relationship between student engagement and performance on online KS on the formative assessment level. Knowledge surveys were developed and formatively administered in four sections of an online Integration of ICT in Education course. Using Moodle Feedback Module, knowledge surveys were designed based on three key elements: learning objectives, the course content, and the revised Bloom’s Taxonomy of learning objectives. Using rated multiple choice KS questions, the correlation between students’ scores on KSs and students’ experience points was calculated using SPSS. The results show that students’ confidence levels in ability to answer KS questions increased in some of the course sections and decreased in others. The student engagement in online course was positively—but weakly—related to student performance on online KS and the strength of this relationship increased as the course unfolded. Our conclusion is that student engagement in online courses would not be an accurate predictor of student performance on online Knowledge surveys right at the beginning of an instructional process.
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