Measuring Peer Mentoring Effectiveness in Computing Courses: A Case Study in Data Analytics for Cybersecurity

Abstract

Computing courses often suffer from lack of diversity. In this paper we evaluate an intervention method of peer mentoring to help increase interest in data analytics in cybersecurity. We present a text mining approach to analyze student assignments while they undergo a peer mentoring exercise. In our prior work, we have shown that the peer mentoring approach is effective at improving the students' interest in cybersecurity careers and contributes to an overall better knowledge gain throughout the semester. This was also reflected by an improvement in grades with two years of anonymous survey results. Across the years we also observed that peer mentoring is particularly effective in diverse groups. In this paper, we perform text mining of the written assignments for analyzing the group behavior of the control and experiment sections of a class while also documenting the effectiveness of intervention methods such as peer mentoring. We employ a few text mining techniques, namely Text Frequency Analysis, Lexical Diversity, Readability Analysis, Hyperlink usage and Objectivity Analysis on the text assignments submitted by the students and show that students who receive peer mentoring are able to express more complex ideas with fewer words and thereby receive higher grades by the end of the semester. Based on these results, we also discuss how our methodology would be applicable in increasing reachability and diversity in other specialized computing courses such as Big Data and distributed systems.

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

“…Table (6): Methods used in Sentiment Analysis in Education Sector Learning Techniques Papers Unsupervised Machine Learning [3], [4], [7], [11], [14], [19], [20], [24], [25], [29], [32], [35], [39], [42], [43], [46], [48], [49], [53], [54], [56], [57], [60], [62], [66], [75], [78], Supervised and unsupervised Machine Learning [9], [10], [23], [27], [28], [31], [34], [36], [37], [41], [44], [63], [64], [65], [69], [70] Supervised Machine Learning [1], [2], [5], [6], [8], [12],…”

Sentiment analysis for formative assessment in higher education: a systematic literature review

“…Summing up, major contributions of SA in higher education assessment are directed towards assessing the emotional climate or status in relation to an educational intervention, so teachers can have timely information to improve students' performance (Dehbozorgi et al, 2020;Md Faridee & Janeja, 2019), predict students' grades (Alblawi & Alhamed, 2017), and dispose of early signs to identify at-risk students (Zhang et al, 2012). We argue that including a gender perspective in developing SA techniques should contribute to the promotion of more equitable assessment.…”

Flagged: Cybersecurity Training and Awareness System Through Virtual Reality

“…Peer Instruction (PI) learning approach is a teaching methodology that encourages active learning and collaboration among students [37,38,39,40,41,42,43]. It involves students learning from and teaching each other under the guidance of an instructor.…”

“…Both studies [38,39] share similarities and differences in their focus and methodology. Both articles explore the application of PI in cybersecurity education, aiming to enhance learning outcomes and student engagement through collaborative learning and active participation.…”

“…However, as mentioned in [38], it specifically examines the impact of PI on learners with different cognitive styles in a VR learning environment. At the same time, the second [39] focuses on measuring the effectiveness of peer mentoring (a form of PI) in data analytics for cybersecurity.…”