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Mouse tracking serves as an alternative to eye tracking in measuring the learning process in education because of its affordability. Moreover, mouse tracking does not require extra hardware, as in the case of eye tracking, because it is a feature in personal computers by default. Therefore, it is possible to implement mouse tracking in a massive open scale. However, mouse tracking has only been implemented in a laboratory setting to date, ostensibly because of the associated extremely high running costs. Nonetheless, there is no available data to support the claim of high resource costs, which has resulted in much speculation among implementers. In general, the implementation of mouse tracking in a non-laboratory environment is still rare. Therefore, the authors developed an application to investigate real-time mouse tracking online. It was implemented on the Moodle learning management system and tested on an online quiz session accessed abroad. Additionally, the application can handle tracking on mobile devices. In this work, the main resources that include CPU, network, RAM, and storage costs were measured when mouse tracking was used. These results can serve as a reference for network and server administrators during future implementation of this technique. It was determined that the characteristics of mouse activities were dynamic in that occasional surges and lulls were observed. Additionally, this article also discussed the advantage of real-time and online implementation to regular online implementation and showed that there is a possibility of implementing mouse tracking on a large scale if mouse tracking data are not aggregated and transmitted as a single data package.
One of the objectives of the performance measurement of gradebased higher education is to reduce the failure rate of students. To identify and reduce the number of failing students, the learning activities and behaviors of students in the classroom must be continuously monitored; however, monitoring a large number of students is an extremely difficult task. A penetration of webbased learning systems in academic institutions revealed the possibility of evaluating student activities via these systems. In this paper, we propose an early prediction scheme to identify students at risk of failing in a blended learning course. We employ a neural network on the set of prediction variables extracted from the online learning activities of students in a learning management system. The experiments were based on data from 1110 student who attended a compulsory, sophomore-level course. The results indicate that a neural-network-based approach can achieve early identification of students that are likely to fail; 25% of the failing students were correctly identified after the first quiz submission. After the mid-term examination, 65% of the failing students were correctly predicted.
E-learning can be a potential solution to educational inequality problem in developing countries, like Mongolia, with vast land and sparse population. With the introduction of Massive Open Online Courses (MOOCs), some impressive cases bring the attention of the public towards it. But there is no specific evidence that shows Mongolian students’ experiences related to these online courses yet. Purpose of this study was to examine Mongolian students’ MOOC perception and experience, especially influence of access, skills, and preferences in their practice. We used a 15-item questionnaire and results were based on 6846 students’ responses. The study population consists of undergraduate students of the National University of Mongolia and high school students from 8 schools in Ulaanbaatar city. There was a significant difference between university and high school students in the awareness and enrollment rates. 47% of the students have heard of MOOCs and 2518 respondents (37%) had an experience of taking MOOCs. Our results show that students have a doubt about MOOC’s academical quality and consider it as an additional source of learning materials. The results of this study can be used to compare students’ perception from other developing countries.
