Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction: An Investigation on Engineering Mathematics Courses

Abstract

A computationally efficient artificial intelligence (AI) model called Extreme Learning Machines (ELM) is adopted to analyze patterns embedded in continuous assessment to model the weighted score (WS) and the examination (EX) score in engineering mathematics courses at an Australian regional university. The student performance data taken over a six-year period in multiple courses ranging from the mid-to the advanced level and a diverse course offering mode (i.e., on-campus, ONC, and online, ONL) are modelled by ELM and further benchmarked against competing models: random forest (RF) and Volterra. With the assessments and examination marks as key predictors of WS (leading to a grade in the mid-level course), ELM (with respect to RF and Volterra) outperformed its counterpart models both for the ONC and the ONL offer. This generated relative prediction error in the testing phase, of only 0.74%, compared to about 3.12% and 1.06%, respectively, while for the ONL offer, the prediction errors were only 0.51% compared to about 3.05% and 0.70%. In modelling the student performance in advanced engineering mathematics course, ELM registered slightly larger errors: 0.77% (vs. 22.23% and 1.87%) for ONC and 0.54% (vs. 4.08% and 1.31%) for the ONL offer. This study advocates a pioneer implementation of a robust AI methodology to uncover relationships among student learning variables, developing teaching and learning intervention and course health checks to address issues related to graduate outcomes, and student learning attributes in the higher education sector. INDEX TERMS education decision-making; Extreme Learning Machine; student performance modelling; AI in higher education; engineering mathematics.

Artificial Intelligence for Student Assessment: A Systematic Review

“…Deo et al [48] determine that AI could be incorporated as a subcategory of intelligent algorithms implemented in stand-alone models, or within a hybrid integrated computer systems model. Both studies agree on the idea that they are systems that prove to be efficient in making decisions similarly to what a human would do.…”

“…Some of them explicitly mention this type of evaluation, while others can be extrapolated from what is described in the work. Three of the papers that do not use formative assessment focus on the use of AI in English language teaching [27,44,50], another two used AI in secondary education [43,47] and the other two in mathematics, but at university level [48,49].…”

Reinventing The Future Online Education Using Emerging Technologies

“…Two scenario examples of using AI in the education sector are: 1) analyzing the learning patterns of the students [14] and 2) evaluating and monitoring online assignments or examinations [15]. In the first scenario, data can be extracted from sensors (e.g., cameras) or from digital footprints from the login and can be used to analyze patterns of students' academic behavior (e.g., frequency of students' login, frequency and duration of the students accessing various materials, or the range of time of assignment submission).…”

The impact of AI implementation in higher education on educational process future: A systematic review

“…Camps et al (2016) concludes that AI is e cient in validating academic credits by a level of 89.4%. Evenly, outcomes vindicate that AI has proven its e ciency in analysing assessments variables and reinforcing intervention for better graduate attributes(Deo et al, 2020). Finally, results pinpoint that AI enables multilingual domains and generates comprehensive, interactive assessments.Impact on Ethics and Future Careers in higher educationResults reveal a crucial need for moral systems.…”