The growth of academic data size in higher education institutions increases rapidly. This huge volume of data collection from many years contains hidden knowledge, which can assist the improvement of education quality and students performance. Students' performance is affected by many factors. In this study, the data used for data mining were students' personal data, education data, admission data, and academic data. NBTree classification technique, one of data mining methods, was adopted to predict the performance of students. Several experiments were performed to discover a prediction model for students' performance. The class labels of students' performance were students' status in study, graduates predicates, and length of study. The experiments were conducted with two-level classification, the university level and faculty level. The resulted model indicated that some attributes had significant influence over students' performance.

Due to its high failure rate, Introductory Programming has become a main concern. One of the main issues is the incapability of slow-paced students to cope up with given programming materials. This paper proposes a learning technique which utilizes pair programming to help slow-paced students on Introductory Programming; each slow-paced student is paired with a fast-paced student and the latter is encouraged to teach the former as a part of  grading system. An evaluation regarding that technique has been conducted on three undergraduate classes from an Indonesian university for the second semester of 2018. According to the evaluation, the use of pair programming may help both slow-paced and fast-paced students. Nevertheless, it may not significantly affect individual academic performance.

Program Visualization (PV) is an educational tool frequently used to assist users for understanding a program flow. However, despite its clear benefits, PV cannot be incorporated easily on Introductory Programming course. Several key properties such as student characteristics and behavior should be considered beforehand. This paper is intended to provide an empirical review about the impact of PV toward students of Introductory Programming course. For our case study, PythonTutor is selected as a sample of PVs due to its accessibility. It can be accessed anywhere and anytime through a web browser. Three conclusions are obtained based on our evaluation on data collected from a survey. Firstly, PV is quite effective to assist students for conducting several programming sub-tasks. Secondly, PV, at some extent, may help students to learn advanced topics on Introductory Programming course. Finally, despite the fact that several features should be incorporated to enhance understanding of students, PV is beneficial for learning Introductory Programming course, especially when it is frequently used.

Based on the fact that the impact of educational tools can only be accurately measured through student-centered evaluation, this paper proposes a long-term in-class evaluation for Python Tutor, a program visualization tool developed by Guo. The evaluation involves 53 students from 4 Basic Data Structure classes, which were held in the even semester of 2016/2017 academic year. It is conducted based on questionnaire survey asked to the students after they have used Python Tutor in their half of programming laboratory sessions. In general, there are three findings from this work. Firstly, Python Tutor helps students to complete programming laboratory tasks, specifically for Basic Data Structure material. Secondly, Python Tutor helps students to understand general programming aspects which are execution flow, variable content change, method invocation sequence, object reference, syntax error, and logic error. Finally, based on student perspectives, Python Tutor is a helpful tool positively affecting the students.

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