Informatics in Education

This research investigates university students’ success in their first programming course (CS1) in relation to their motivation, mathematical ability, programming self-efficacy, and initial goal setting. To our knowledge, these constructs have not been measured in a single study before in the Finnish context. The selection of the constructs is in line with the statistical model that predicts student performance (“PreSS”) (Quille and Bergin, 2018). The constructs are compared with various demographic and background variables, such as study major, prior programming experience, and average weekly working hours. Some of the main results of this study are as follows: (1) students generally entered with a high interest in programming and high motivation, but these factors did not increase during the course, i.e., interest in programming did not increase. (2) Having prior experience yielded higher initial programming self-efficacy, grade expectations, and spending less time on tasks, but not better grades (although worse neither). While these results can be seen as preliminary (and alarming in some parts), they give rise to future research for investigating possible expectation–performance gaps in CS1 and later CS studies. As our dataset accumulates, we also hope to be able to construct a valid success prediction model.

This paper proposes and validates a short and simple Expectancy-Value-Cost scale, called EVC Light. The scale measures the motivation of students in computing courses, allowing the easy and weekly application across a course. One of the factors related directly to the high rate of failure and dropout in computing courses is student motivation. However, measuring motivation is complex, there are several scales already carried out to do that job, but only a few of them consider the longitudinal follow-up of motivation throughout the courses. The EVC Light was applied to 245 undergraduate students from four universities. The Omega coefficient, scale items intercorrelation, item-total correlation, and factor analysis are used to validate and measure the reliability of the instrument. Confirmatory and exploratory factor analyses supported the structure, consistency, and validity of the EVC Light scale. Moreover, a significant relationship between motivation and student results was identified, based mainly on the Expectancy and Cost factors.

Computerized Adaptive Testing (CAT) is now widely used. However, inserting new items into the question bank of a CAT requires a great effort that makes impractical the wide application of CAT in classroom teaching. One solution would be to use the tacit knowledge of the teachers or experts for a pre-classification and calibrate during the execution of tests with these items. Thus, this research consists of a comparative case study between a Stratified Adaptive Test (SAT), based on the tacit knowledge of a teacher, and a CAT based on Item Response Theory (IRT). The tests were applied in seven Computer Networks courses. The results indicate that levels of anxiety expressed in the use of the SAT were better than those using the CAT, in addition to being simpler to implement. In this way, it is recommended the implementation of a SAT, where the strata are initially based on the tacit knowledge of the teacher and later, as a result of an IRT calibration.

Students' performances in introductory programming courses show large variation across students. There may be many reasons for these variations, such as methods of teaching, teacher competence in the subject, students' coding backgrounds and abilities, students' self-discipline, the teaching environment, and the resources available to students, all of which can affect student performance and outcomes. Our observations in teaching programming courses (at Al-Imam Muhammad Ibn Saud Islamic University in Riyadh) are that many students (up to 50% per course) drop out. There is a strong belief by many instructors that such a high dropout rate is due, at least in part, to students underestimating the effort needed to finish this course and not following instructions as recommended. This paper reviews the factors that affect student performance in an introductory programming course (CS1) and aims to discover correlations between various assessment methods, students' participation and their final performance measured. It analyses mark distributions across various assessment methods to identify which assessment method best predicts final exam marks and overall marks, and gives recommendations for assessment in introductory programming courses.

Games for learning are currently used in several disciplines for motivating students and enhancing their learning experience. This new approach of technology-enhanced learning has attracted researchers' and instructors' attention in the area of programming that is one of the most cognitively demanding fields in Computer Science. Several educational, or else serious, games for learning programming have been developed and the first results of their evaluation as a means of learning are quite positive. In this paper, we propose using arcade games as a means for learning programming. Based on this approach students first play a simple game, such as Snake or Tetris, study its code and then extend it. In a pilot study carried out in the context of an undergraduate programming course, students studied the source code of the well-known game Snake and extended it with new functionalities. The analysis of students' replies in a questionnaire showed that using arcade games as a means of learning programming concepts enhances students' motivation for learning programming, supports them in comprehending complex concepts and engages them in carrying out programming activities.