<span lang="EN-US">This paper introduces a set of extracted factors from Moodle log file of the selected course as a case study that aims to capture student Engagement (E), Behavior (B), Personality (Pers) and Performance (P). The factors are applied to identify students’ EBPersP with different course activities. The data set used in this paper was selected from the "Introduction to Computer Science" online course that captures 273,906 records as a log file for 29 students, delivered in Spring 2020. The paper also tries to show whether there is a relationship between student engagement, behavior and personality and their performance. Results show different patterns of students’ interactions with course contents, activities, and assessments. Specifically, our findings highlight that students' EBPersP could be extracted from Moodle log files. In addition, the extracted factors could assist instructors on how to focus more on students with low and average performance, giving them more attention to enhancing their performance. </span>
One of the most important pillars of smart cities is the smart learning environ-ment. This environment should be well prepared and managed to improve the in-struction process for instructors from one side and the learning process for stu-dents from the other side. This paper presents the student’s Engagement, Behav-ior and Personality (EBP) predictive model. This model uses Moodle log data to investigate the influence and the effect of the students’ EBP factors on their per-formance. For this purpose, this paper uses the data log files of the "Search Strat-egies on the Internet" online course in Fall 2019 at Sultan Qaboos University (SQU) extracted from Moodle database. The intention of conducting this kind of experiments is of three-facets: 1. to assist in gaining a holistic understanding of online learning environments by focusing on student EBP and performance with-in the course activities, 2. to explore whether the student’s EBP can be considered as indicators for predicting student’s performance in online courses, and 3. to support instructors with insights to develop better learning strategies and tailor instructions for personal learning of individual students. Moreover, this paper takes a step forward in identifying effective methods to measure student’s EBP during the learning process. This may contribute to proposing a framework for the smart learning behavior environment that would guide the instructors to ob-serve students’ performance in a more creative way. All the 38 students who participated in this experiment had compatible statistics and results as the relationship between their Engagement, Behavior, Personality was symmetric with their Performance. This relationship was presented using a group of condition rules (If-then). The extracted rules gave us a straightforward and visual picture of the rela-tionship between the factors mentioned in this paper.
Our motivation in this paper is to predict student Engagement (E), Behavior (B), Personality (P) and Performance (P) via designing a Tracking Student Perfor-mance Tool (TSPT) that obtained data directly from Moodle logs of any selected courses. The proposed tool follows the predictive EBP model that focuses mainly on student's EBP and Performance where the instructor could use it to monitor the overall performance of his/her students during the course. The results of test-ing the tool show that the developed tool gives the same as manual results analy-sis. Analyzing Moodle log of any course using such a tool is supposed to help with the implementation of similar courses and helpful for the instructor in re-designing it in a way that is more beneficial to the students. This paper sheds light on the importance of studying student's EBPP and provides interesting possibili-ties for improving student performance with a specific focus on designing online learning environments or contexts.
Using a systematic framework could enhance the educational atmosphere to be creative. It is very important to use smart technology, which integrates innovative technology features to enhance the learning process. This could be taken care of by adopting an effective tool to create a smart educational environment and an effective personalized learning system. To create a smart educational environment, it requires smart tools to help instructors to carefully prepare the learning process, creative content of learning and appealing lessons. Also, smart tools are needed to demonstrate the results of learning in an attractive manner. This paper aims to propose a systematic framework called "SQU-SLMS" for developing a smart learning environment. It uses an existing LMS (i.e. Moodle), which is used at Sultan Qaboos University (SQU). Also, it uses a smart technical-predictive model, which is an incorporated component into a Smart Learning Management System (SLMS). The proposed technical framework consists of several components layers: users, user interface management authentication and authorization, smart aspects and database. The proposed SQU-SLMS has been compared with existing frameworks and ISC/IEC 12207:2008 and ISO/IEC 19796-1 standards. Also, the proposed systematic framework has been evaluated using ISO/IEC 9126-1. One of the outstanding outcomes that can be of a great value to instructors, is that the proposed SQU-SLMS framework can be used as an indicator that supports their teaching and learning process to keep monitoring student performance in a smart way and with less effort.
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