An Advanced eLearning Environment Developed for Engineering Learners

Abstract

Abstract-Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented.

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

“…Evaluation of student understanding, engagement and academic integrity Three articles reported on student-facing tools that evaluate student understanding of concepts (Jain, Gurupur, Schroeder, & Faulkenberry, 2014;Zhu, Marquez, & Yoo, 2015) and provide personalised assistance (Samarakou, Fylladitakis, Früh, Hatziapostolou, & Gelegenis, 2015). Hussain et al (2018) used machine learning algorithms to evaluate student engagement in a social science course at the Open University, including final results, assessment scores and the number of clicks that students make in the VLE, which can alert instructors to the need for intervention, and Amigud, Arnedo-Moreno, Daradoumis, and Guerrero-Roldan (2017) used machine learning algorithms to check academic integrity, by assessing the likelihood of student work being similar to their other work.…”

“…Using academic data to monitor and guide students The adaptive systems within this category focus on the extraction of student academic information to perform diagnostic tasks, and help tutors to offer a more proactive personal guidance (Rovira, Puertas, & Igual, 2017); or, in addition to that task, include performance evaluation and personalised assistance and feedback, such as the Learner Diagnosis, Assistance, and Evaluation System based on AI (StuDiAsE) for engineering learners (Samarakou et al, 2015).…”

The Impact of Artificial Intelligent in Education toward 21st Century Skills: A Literature Review

“…Increasing adaptability and interactivity in digital environments mean in order to create digital environments that are more adaptable, AI technologies have been used to collect data on student learning and make interactions easier. Samarakou et al (2015) created an advanced environment for e-learning for engineering students. Westera et al (2020) used techniques like automatic difficulty adaptation, stealth assessment, and facial emotion recognition to profile students.…”

Apoyo de las tecnologías de IA en el aprendizaje, la enseñanza, y la evaluación en los contextos de la educación

“…Las tecnologías de IA han sido implementadas para recolectar datos de aprendizaje y mejorar las interacciones en ambientes digitales más adaptables. Samarakou et al (2015) desarrollaron un entorno de aprendizaje electrónico avanzado para estudiantes de ingeniería. Kickmeier y Holzinger (2019) crearon un algoritmo de optimización combinatoria (el sistema de hormigas MAXMIN) que demostró ser efectivo en juegos educativos adaptativos.…”