Nowadays Information and Communication Technologies (ICTs) are widely used in a variety of fields all over the world. As for instance, use of ICT in education has transformed the way of delivering instructions from a traditional to modern and effective manner. As Afghanistan proceeds to move forward the higher instruction openings for its citizens, instruction through cutting edge strategies and methods such as e-learning must be considered. Kabul Polytechnic University (KPU) has consistently aligned itself with the Ministry of Higher Education's strategic plan and has taken major steps towards the implementation of elearning. Recently, a pilot implementation of Moodle has taken place. This paper provides (i) an overview of ICT for education (ICT4E) in Afghanistan; (ii) evaluates the current status of elearning at KPU; (iii) reports on pilot implementation of Moodle; (iv) identifies the potential challenges and (v) proposes possible solutions to overcome the challenges.

Learning performance is crucial in students’ academic lives because it opens opportunities for future professional development. However, conventional educational practices do not provide all the necessary skills for university instructors and students to succeed in today's educational context. In addition, due to poor information resources, ineffective ICT tool utilization and the teaching methodologies in developing countries, particularly Afghanistan, a large gap exists across curriculum plans and instructor practices. Learning analytics, as a new educational instrument, has made it possible for higher education actors to reshape the educational environment to be more effective and consistent. In this study, we analyzed multiple research approaches and the results of analytics of various learner aspects to address the aforementioned issues. The research methods were predominantly quantitative-cum-qualitative. Real (quantitative) data were collected based on learners’ explicit actions, such as completing assignments and taking exams, and implicit actions, such interacting and posting on discussion forums. Meanwhile, secondary (qualitative) data collection was conducted on-site at Kabul Polytechnic University (KPU); both blended and traditional class samples were included. The results of this study offer insight into various aspects of learners’ behaviors that lead to their success and indicate the best analytical model/s to provide the highest prediction accuracy. Furthermore, the results of this study could help educational organizations adopt learning analytics to conduct early assessments to evaluate the quality of teaching and learning and improve learners’ performance.

The last two decades have witnessed a global revolution in educational information that has led to the development and promotion of e-learning. Blended Learning (BL) is an increasingly growing e-learning model with a background in pedagogical and psychological theory that combines both online and traditional activities. In recent years, it has been an emerging trend and has impacted the growth, revenue, learner retention, and academic accreditation in higher education. With current improvements, extensive research, and successful implementation of blended and fully online learning, little research has been done to report the success of transitioning from face-to-face to blended learning or evaluations of e-learning data regarding learners from developing nations, particularly Afghanistan. This study aims to investigate and analyze the effectiveness of educational types (blended vs. traditional) regarding learners’ academic performance, in-class engagement, and satisfaction from the data in six BL courses and four traditional learning (TL) courses. To measure the success, this study used descriptive statistics. Additionally, Welch’s t-test was used to compare BL with TL courses and assess the differences between success and failure levels for both courses. Likewise, the Pearson correlation coefficient, along with an ordinary least square regression, was used to indicate the relationship between the final score and the BL and TL activities, respectively. The study outcome will be used for reporting and feedback for educational parties to value the quality of teaching and learning, enhance learners’ performances, and for the institutionalization of BL in the country.

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In this paper, the authors present a predictive model for failure-prone students using access log data from two small datasets in the Moodle learning system. Although various advanced machine learning algorithms, especially supervised predictive methods, can be used with very large datasets, these tools are often not available for most initial university research, especially in developing countries, to predict learners’ future outcomes. The authors examined the use of students’ access patterns to track failure-prone students so that early interventions could be made to prevent failure or dropout. Real data were collected through explicit learners’ actions, such as completing assignments and taking quizzes, from two compulsory blended courses, Operating System (junior level, or third year) and System Analysis and Design (sophomore level, or second year). Research methods were predominantly quantitative. The proposed models correctly predicted failure-prone students before the end of the second academic month (fifth week) for both courses (88% of the class for juniors and 86% of the class for sophomores), which made it possible to intervene early and provide required support during the semester. Similarly, the study outcomes showed the students’ past performance, specifically their grade point average, could affect their final performance. The outcomes of this study can be used to analyze the behaviors of learners that lead to high success and high retention rate. Furthermore, the study results will be used to report and provide feedback to the educational parties to value the quality of teaching and learning, the improvement of course materials, and increasing learner success.