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.
Highlights
The rapid technological progress of computers and telecommunications during the past few decades, and the adoption rate of home computers and the internet, allowed for the development of educational methods that were implausible some years ago
A large portion of the research on new educational methods is focused on the development of Open Learning Environments (OLEs), especially by higher education institutions that seek to deliver their educational material with limited supervision and or remotely [1,2,3,4,5,6]
We present the Learner Diagnosis, Assistance, Evaluation System based on Artificial Intelligence (StuDiAsE), an advanced OLE developed to cater for the needs of engineering learners
Summary
The rapid technological progress of computers and telecommunications during the past few decades, and the adoption rate of home computers and the internet, allowed for the development of educational methods that were implausible some years ago. Even in conditions where the learners were expected to apply their theoretical knowledge to perform experiments and or exercises, the educational approach was based on a "one size fits all" scheme and the evaluation was based solely and quantitatively on the final result [9, 10] This approach does not provide feedback to the learners and restricts the tutors, as the process followed by the learners and any of their strengths or weaknesses are opaque to the tutor. StuDiAsE is based on the text comprehension theory by Denhière & Baudet [27] and dialogue theory [28], and is capable of monitoring the comprehension of the learners, assess their prior knowledge, construct individual educational profiles, provide personalized assistance, and evaluate a learner's performance both quantitatively and qualitatively through artificial intelligence [29,30,31]. In paragraph IV we provide the results of a case study that has been performed with the participation of 60 learners over four courses
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