## 6 Conclusions and Future Works  
In this study, a supervised data classification model is proposed with the aim of predicting student academic performance at the end of the semester. Two modules were selected for the study based on the similarity of the course content. The dataset consisted of student academic data gathered from SIS (student academics) and performance in the modules using two different learning environments; i.e., Moodle (LMS) and eDify (mobile application). Activities performed by students in Moodle on campus and outside campus were used. Video interactions (clickstreams) of students in eDify were used for prediction. In total, 18 features and one meta attribute were used to form the dataset, 12 features were extracted from SIS, 2 from Moodle, four from eDify and one result was used as the target feature for prediction. The dataset consisted of 772 samples from one academic year. The complete dataset was tested with eight classification algorithms derived from the literature review and related works.  
The Tree-based classification model--specifically, Random Forest--outperformed the other techniques with an accuracy of 88.3%. This accuracy was achieved using the equal width data transformation method and information gain ratio selection technique. To identify which features have greater importance in the prediction of student performance, features were reduced using the genetic algorithm and PCA. The result was inconclusive as the reduced features have low significance in predicting student performance. Multivariate analysis was conducted to inspect the correlations, and nine variables were selected using scoring and ranking techniques to successfully predict the students' academic performance. Thus, the results obtained with reduced features were better than those using all the features. The CN2 Rule Inducer algorithm was the second-best performing algorithm, showing 87.4% accuracy. The reason for using CN2 Rule Viewer is that it provides rules induction with probability, which is easier to interpret for non-expert users such as faculty who can therefore easily relate to the situation, as shown in Table 12.  
For future work, a dashboard with data representations from these virtual learning environments would help in projecting the students' performance and interactions. Predicting the students' performance and outcomes on a weekly basis could help faculty to identify poor-performing students. This can act as an early alert system for faculty to intervene with any problems faced by the students within the module. Students could also self-assess their own performance within the module with the help of a dashboard.  
[PERSON] contributed to the investigation and project administration. [PERSON] contributed to the supervision- [PERSON] contributed to the visualization. [PERSON] contributed to the resources and writing--review and editing. [PERSON] and [PERSON] contributed to the collected data and conducted the pre-processing of the input data. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank Middle East College, Oman for the experimental data. The authors are thankful to the Head of Computing Department, [PERSON] (MEC), for his support and encouragement to carry out this study.  
The authors declare no conflict of interest.