A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

Can gamification help in software testing education? Findings from an empirical study

Software testing is an essential knowledge area required by industry for software engineers. However, software engineering students often consider testing less appealing than designing or coding. Consequently, it is difficult to engage students to create effective tests. To encourage students, we explored the use of gamification and investigated whether this technique can help to improve the engagement and performance of software testing students. We conducted a controlled experiment to compare the engagement and performance of two groups of students that took an undergraduate software testing course in different academic years. The experimental group is formed by 135 students from the gamified course whereas the control group is formed by 100 students from the non-gamified course. The data collected were statistically analyzed to answer the research questions of this study. The results show that the students that participated in the gamification experience were more engaged and achieved a better performance. As an additional finding, the analysis of the results reveals that a key aspect to succeed is the gamification experience design. It is important to distribute the motivating stimulus provided by the gamification throughout the whole experience to engage students until the end. Given these results, we plan to readjust the gamification experience design to increase student engagement in the last stage of the experience, as well as to conduct a longitudinal study to evaluate the effects of gamification

Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise

The teaching distribution for lecturers based on their expertise is very important in the teaching and learning process. Lecturers who teach a course that is in accordance with their interests and abilities will make it easier for them to deliver material in class. In addition, students will also be easier to accept the material presented. However, in reality, the teaching distribution is often not in accordance with the expertise of the lecturer so that the lecturers are not optimal in providing material to their students. This problem can be solved using optimization methods such as the genetic algorithm. This study offers a solution for teaching distribution that focuses on the interest of each lecturer by considering the order of priorities. The optimal parameters of the test results are crossover rate (cr) = 0.6, mutation rate (mr) = 0.4, number of generations = 40, and population size = 15. Genetic algorithm is proven to be able to produce teaching distribution solutions with a relatively high fitness value at 4903.3

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

IMPRESS: Improving Engagement in Software Engineering Courses through Gamification

Software Engineering courses play an important role for preparing students with the right knowledge and attitude for software development in practice. The implication is far reaching, as the quality of the software that we use ultimately depends on the quality of the people that make them. Educating Software Engineering, however, is quite challenging, as the subject is not considered as most exciting by students, while teachers often have to deal with exploding number of students. The EU project IMPRESS seeks to explore the use of gamification in educating software engineering at the university level to improve students' engagement and hence their appreciation for the taught subjects. This paper presents the project, its objectives, and its current progress