Computer programming skills have been growing as a professional competence also to unqualified end-users who need to develop software in their professional practice. Quality evaluation models of end-user-developed products are still scarce. In this paper, we propose a metric that leverages “When”, a condition typically found in block-based software development frameworks. We evaluated 80 Scratch projects collecting a metric related to the presence of the When condition and investigated common traits and differentiation with other metrics already proposed in the literature. We found that, in an evaluation with respect to the conditionals found in Scratch projects, When delivers a distinct and complementary approach to software complexity in products developed using block-oriented software development tools.
This study investigated the role of using unplugged computing activities on developing computational thinking (CT) skills of 6th-grade students. The unplugged computing classroom activities were based on the Bebras challenge, an international contest that aims to promote CT and informatics among school students of all ages. Participants of the study were fifty-three 6th-grade students from two public middle schools in Istanbul. The unplugged computing activities involved the tasks with three different difficulty levels covering the CT processes found to be common in CT definitions in the literature. To evaluate students’ CT skills, two equivalent tests were constructed from Bebras tasks considering the same parameters (difficulty levels and CT processes). The results showed that students’ post-test scores were significantly higher than their pre-test scores. There were not any significant differences between students’ scores in terms of gender, and there was no interaction effect between students’ CT scores and their gender.
This work presents a systematic review whose objective was to identify heuristics applicable to the evaluation of the usability of educational games. Heuristics are usability engineering methods that aim to detect problems in the use of a system during its development and / or when its interface is in interaction with the user. Therefore, applying heuristics is an essential part of developing digital educational games. Search sources were articles available in all the databases present in the Capes / MEC / Brazil periodicals portal, in the available languages. The descriptors adopted were "educational games", "heuristic" and "usability" in Boolean search in titles, abstracts and keywords, with AND operator, for publications starting in 2014. The inclusion criteria were: (a) articles with a clear description of the methodology used in the usability analysis; (b) studies presenting primary data and (c) articles whose focus corresponds to the investigated question. Two examiners conducted the searches in the databases and a third the evaluation and general review of the data. Initially, 93 articles were identified, of which 19 were repeated, 5 were literature reviews. Of the 69 that remained, 57 were elected as not eligible with only 12 selected for full studies, of which 6 entered the final review. With this review we can deduce that the field of heuristics and usability for educational games is still little explored, with few specific evaluations validated or in the process of validation, requiring greater investment in the area. Through this review, we found at least one heuristic that meets the usability evaluation of educational software: Game User Experience Satisfaction Scale (GUESS).
The purpose of the study is to examine the effect of unplugged coding activities carried out with middle school students on their computational thinking skills. This study employed nested-mixed design, which is a mixed research method; the data were supported by including the qualitative phase into an experimental study. In this frame, a group of 114 middle school students consisting of 5th graders were given coding training titled "Kesfet Project - I Discover Coding" by using unplugged coding content. The Computational Thinking Scale was applied to the students at the beginning and end of the training; the results obtained from the scale were analyzed by means of a paired t test. Finally, it was found out that unplugged coding activities had a positive effect on the improvement of computational thinking skills of the students. An examination of the sub-factors revealed that there is statistically no significant change in the problem solving skill despite the positive impact observed on creativity, algorithmic thinking, collaboration and critical thinking skills. Following the analysis of observation and daily data, the findings obtained revealed that the students usually displayed high levels of motivation and class participation in unplugged coding activities, they had difficulty in concretizing certain concepts as well as subjects requiring mathematical knowledge; various teaching methods and techniques were used in classes; the students liked the activities especially due to their appealing nature and their relation to daily life; however, there were occasional problems with scheduling of activities and teamwork due to over-crowded class size; the students experienced problems in achieving outcomes such as perceiving the relationship between computer science and mathematics and analyzing the given problem, and could have difficulty in associating between computer science and mathematics or between the subjects learned and the computer lesson, and in analyzing a given problem.
Technology-enhanced learning generally focuses on the cognitive rather than the affective domain of learning. This multi-method evaluation of the INBECOM project (Integrating Behaviourism and Constructivism in Mathematics) was conducted from the point of view of affective learning levels of Krathwohl et al. (1964). The research questions of the study were: (i) to explore the affective learning experiences of the three groups of participants (researchers, teachers and students) during the use of a mobile game UFractions and an intelligent tutoring system ActiveMath to enhance the learning of fractions in mathematics; and (ii) to determine the significance of the relationships among the affective learning experiences of the three groups of participants (researchers, teachers and students) in the INBECOM project.
This research followed a sequential, equal status, multi-mode research design and methodology where the qualitative data were derived from the interviews with researchers, teachers and students, as well as from learning diaries, feelings blogs, and observations (311 documents) across three contexts (South Africa, Finland, and Mozambique). The qualitative data was quantitized (Saldaña, 2009), i.e. analysed deductively in an objective and quantifiable way as instances on an ExcelT spreadsheet for statistical analyses. All the data was explored from the affective perspective by labelling the feelings participants experienced according to the affective levels of the Krathwohl et al. (1964) framework.
The researchers concluded that: (i) the research participants not only received information, but actively participated in the learning process; responded to what they learned; associated value to their acquired knowledge; organised their values; elaborated on their learning; built abstract knowledge; and adopted a belief system and a personal worldview; and (ii) affirmation of affective learning at all five levels was recognised among the three groups of participants. The study raised a number of issues which could be addressed in future, like how affective levels of learning are intertwined with cognitive levels of learning while learning mathematics in a technology-enhanced learning environment; and how pedagogical models which take into account both cognitive and affective aspects of learning support deep learning.
Problem solving skills are considered an important component in learning to program in an introductory programming (IP) course for novices. This study introduced a PROSOLVE game to enhance problem solving skills of novice programmers in the introductory programming course. The game is based on pseudo-code technique. A survey was employed to collect students' feedback and semi-structured interviews were organized to collect instructors' opinion about the game. The results show that the game helped most of the students in understanding the programming concepts, structures and problem solving strategies. Moreover, the game supports students' cognitive engagement, gains, and affective engagement in the IP course. Instructors appreciated the game and considered it as an additional supporting teaching tool in the IP course. Moreover, they considered the game as good alternative of traditional pen and paper learning approach in attracting students' interest in the programming domain.
The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for them. First of all, systematic literature review on application of CBR and its possible implementation to personalise learning was performed in the paper. After that, methodology on CBR application to personalise learning is presented where learning styles play a dominate role as key factor in proposed personalised intelligent learning system model based on students profiling and personalised learning process model. The algorithm (the sequence of steps) to implement this model is also presented in the paper.
Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these techniques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these techniques. Specifically, the dataset is designed for evaluation with an Information Retrieval (IR) perspective. The dataset consists of 467 source code files, covering seven introductory programming assessment tasks. Unique to this dataset, both intention to plagiarise and advanced plagiarism attacks are considered in its construction. The dataset's characteristics were observed by comparing three IR-based detection techniques, and it is clear that most IR-based techniques are less effective than a baseline technique which relies on Running-Karp-Rabin Greedy-String-Tiling, even though some of them are far more time-efficient.
The aim of the present study was to investigate the properties of paper-and-pencil data collection instruments developed to measure Computational Thinking (CT) based on several variables. Thus, keywords were identified and used in searches conducted in various databases. The outcomes of the search were analyzed based on the inclusion/exclusion criteria and 64 studies that focused on CT measurement were identified. Content analysis findings were classified under several themes. Based the present study findings, it was determined that the number of tools developed to measure CT demonstrated an increasing trend over time. Furthermore, it was found that the above-mentioned studies included mainly tests. Moreover, it was observed that the processes of ensuring validity and reliability were not clearly specified for more than half of the paper-and-pencil data collection instruments designed to measure CT. Based on the findings, several recommendations were presented for future studies and implementations in the related field.
The aim of this study was to investigate the factors affecting the pre-service computer science teachers' attitudes towards computer programming (ATCP). The sample consists of 119 pre-service teachers at a public state university. The influences of students' demographic characteristics (gender, grade level, and high school type), their achievement in computer programming courses, perceived learning, and computer programming self-efficacy on their ATCP were tested using multiple linear regression. Descriptive, correlation and regression analyses revealed three findings: (1) students had moderately high ATCP, (2) their ATCP had significant correlations with their achievement in computer programming courses, computer programming self-efficacy, and perceived learning, and (3) three variables (achievement in computer programming courses, computer programming self-efficacy, and perceived learning) were significant predictors of their ATCP.