First-Year Engineering Students’ Strategies for Taking Exams

Abstract

Student drop-out is one of the most critical issues that higher educational institutions face nowadays. The problem is significant for first-year students. These freshmen are especially at risk of failing due to the transition from different educational settings at high school. Thanks to the massive boom of Information and Communication Technologies, universities have started to collect a vast amount of study- and student-related data. Teachers can use the collected information to support students at risk of failing their studies. At the Faculty of Mechanical Engineering, Czech Technical University in Prague, the situation is no different, and first-year students are a vulnerable group similar to other institutions. The most critical part of the first year is the first exam period. One of the essential skills the student needs to develop is planning for exams. The presented research aims to explore the exam-taking patterns of first-year students. Data of 361 first-year students have been analysed and used to construct “layered” Markov chain probabilistic graphs. The graphs have revealed interesting behavioural patterns within the groups of successful and unsuccessful students.

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

“…Learning Engagement [22], [34], [39], [40], [42], [43], [46], [49], [52], [53], [58], [67]- [70], [76], [87] In accordance with the findings presented in [23], academic performance pertains to the achievement of students in their pursuit of higher education, typically assessed through their academic grades or performance on examinations and assessments [18], [23], [35], [45], [64], [80], with particular emphasis on their initial year of study. Additionally, it encompasses the probability of students' persistence in pursuing further education or their decision to discontinue their academic pursuits.…”

“…The discipline of learning analytics encompasses various dimensions and focuses on the methodical gathering, examination, and presentation of data regarding learners and their educational context. The principal objective is to comprehend and enhance both the process of acquiring knowledge and the environment in which it occurs [34], [39], [40], [43], [52], [58], [69], using advanced artificial intelligence techniques [68], [76].…”

“…Data Source Paper 1 LMS [1], [9], [11]- [14], [19], [35]- [40], [43]- [45], [54], [58], [63], [65], [66], [68]- [71], [73], [74], [77], [79], [84], [87] 2 SIS [15], [17], [21], [22], [54]- [56], [62], [76], [78], [85], [86] 3 Hybrid/LMS and SIS [10], [18], [20], [23], [24], [34], [46]- [53], [57], [59]- [61], [72], [72], [75], [81], [82] 4 Other [41], [42],…”

“…Table 6 provides a concise summary of these findings. [13], [34], [35], [37]- [40], [42], [44], [45], [54], [62], [72], [76], [78], [86] 2 Behavioral [11], [19], [24], [43], [58], [60], [63]- [65], [69], [71], [73] 3 Academic and Behavioral [1], [10], [12], [14], [18], [46]- [49], [51], [53], [57], [68], [74], [77], [79], [80], [84] 4 Academic and Demographic [17], [36], [56] 5 Academic, Demographic and Pre-University…”

You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education

“…Currently, we are seeing a significant expansion of AI-related resources in higher education in elements embedded into learning management systems, student information systems, test generators, plagiarism-detection systems, and accessibility products [27,28]. The application of these intelligent resources allows for detecting at-risk students, analyzing their metrics, and designing tailored learning adjusted to their needs and academic trajectories [29][30][31][32][33].…”