Automatic Identification of Ineffective Online Student Questions in Computing Education

This Research Full Paper explores automatic identification of ineffective learning questions in the context of large-scale computer science classes. The immediate and accurate identification of ineffective learning questions opens the door to possible automated facilitation on a large scale, such as alerting learners to revise questions and providing adaptive question revision suggestions. To achieve this, 983 questions were collected from a question & answer platform implemented by an introductory programming course over three semesters in a large research university in the Southeastern United States. Questions were firstly manually classified into three hierarchical categories: 1) learning-irrelevant questions, 2) effective learning-relevant questions, 3) ineffective learningrelevant questions. The inter-rater reliability of the manual classification (Cohen's Kappa) was .88. Four different machine learning algorithms were then used to automatically classify the questions, including Naive Bayes Multinomial, Logistic Regression, Support Vector Machines, and Boosted Decision Tree. Both flat and single path strategies were explored, and the most effective algorithms under both strategies were identified and discussed. This study contributes to the automatic determination of learning question quality in computer science, and provides evidence for the feasibility of automated facilitation of online question & answer in large scale computer science classes

Identification of critical time‐consuming student support activities in e‐learning

Higher education staff involved in e‐learning often struggle with organising their student support activities. To a large extent this is due to the high workload involved with such activities. We distinguish support related to learning content, learning processes and student products. At two different educational institutions, surveys were conducted to identify the most critical support activities, using the Nominal Group Method. The results are discussed and brought to bear on the distinction between content‐related, process‐related and product‐related support activities

Collaboration Versus Cheating

We outline how we detected programming plagiarism in an introductory online course for a master's of science in computer science program, how we achieved a statistically significant reduction in programming plagiarism by combining a clear explanation of university and class policy on academic honesty reinforced with a short but formal assessment, and how we evaluated plagiarism rates before SIGand after implementing our policy and assessment.Comment: 7 pages, 1 figure, 5 tables, SIGCSE 201

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Security and Online learning: to protect or prohibit

The rapid development of online learning is opening up many new learning opportunities. Yet, with this increased potential come a myriad of risks. Usable security systems are essential as poor usability in security can result in excluding intended users while allowing sensitive data to be released to unacceptable recipients. This chapter presents findings concerned with usability for two security issues: authentication mechanisms and privacy. Usability issues such as memorability, feedback, guidance, context of use and concepts of information ownership are reviewed within various environments. This chapter also reviews the roots of these usability difficulties in the culture clash between the non-user-oriented perspective of security and the information exchange culture of the education domain. Finally an account is provided of how future systems can be developed which maintain security and yet are still usable

A Survey of Smart Classroom Literature

Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis

Doctor of Philosophy

dissertationSelf-regulated learning with online resources is a prevalent experience for today's learners, but these online learning opportunities frequently yield disappointing results when considering students' learning outcomes. The current research examined the impact of different forms of navigational scaffolds to help learners self-regulate their learning behaviors as they attempted to form well-organized, conceptual knowledge from varied online resources. Experiment 1 examined scaffolds for two potentially useful learning paths: conceptual coherence (depicted in a graphical overview of the domain) and foundational knowledge (depicted via visual cues about the importance of a concept to the domain). Results revealed no effects of a conceptual coherence scaffold on participants' self-regulated learning behaviors or learning outcomes. When foundational knowledge scaffolds were present, participants used more effective self-regulated learning strategies on higher priority concepts, but learning did not improve. Participants utilized prescribed learning paths only 63% of the time and thus may not have benefited from them. Experiment 2 investigated the impact of using a dynamic, automatic scaffold to structure learning paths through the online resources; both learning path (coherence vs. foundational) and amount of learner navigational control (low vs. high) were varied. Results revealed that when a foundational knowledge path was enforced, learners executed more effective self-regulated learning strategies and gained a deeper understanding of conceptual relationships. Overall findings suggest that learners working with digital resources benefit from navigational guidance that helps them focus on foundational ideas in an online, self-regulated environment