Nondeterminism (ND) is a fundamental concept in computer science, and comes in two main flavors. One is the kind of ND that appears in automata theory and formal languages, and is the one that students are usually introduced to. It is known to be hard to teach. We present here a study, in which we introduced students to the second kind of ND, which we term operative. This kind of ND is quite different from the first one. It appears in nondeterministic programming languages and in the context of concurrent and distributed programming. We study how high-school students understand operative ND after learning the nondeterministic programming language of live sequence charts (LSC). To assess students' learning, we used a two-dimensional taxonomy that is based upon the SOLO and the Bloom taxonomies. Our findings show that after a semestrial course on LSC, high-school students with no previous experience with ND of either type, understood operative ND on a level that allowed them to create and execute programs that included nondeterminism on various levels and in various degrees of complexity. We believe that it is important to expose students to the two types of ND, especially as ND has become a very prominent characteristic of computerized systems. Our findings suggest that students can reach a significant understanding of operative ND when the concept is introduced in the context of a programming course.
As an international informatics contest, or challenge, Bebras has started the second decade of its existence. The contest attracts more and more countries every year, recently there have been over 40 participating countries. From a single contest-focused annual event Bebras developed to a multifunctional challenge and an activities-based educational community building model. This paper aims to introduce the Bebras model using ten years of observations in implementing the contest in different countries. The model is essentially based on democratic and inclusive education values. Systematic literature review of research papers concerning Bebras activities has made an integral background for this model. The model is represented both at international and national levels and consists of several components where the development of Bebras tasks has taken a very significant role. Reasoning on innovated learning informatics and strengthening computational thinking by utilising carefully selected informatics concepts is discussed as well.
Content personalization in educational systems is an increasing research area. Studies show that students tend to have better performances when the content is customized according to his/her preferences. One important aspect of students particularities is how they prefer to learn. In this context, students learning styles should be considered, due to the importance of this feature to the adaptivity process in such systems. Thus, this work presents an efficient approach for personalization of the teaching process based on learning styles. Our approach is based on an expert system that implements a set of rules which classifies learning objects according to their teaching style, and then automatically filters learning objects according to students' learning styles. The best adapted learning objects are ranked and recommended to the student. Preliminary experiments suggest promising results.
The use of computers as teaching and learning tools plays a particularly important role in modern society. Within this scenario, Brazil launched its own version of the 'One Laptop per Child' (OLPC) program, and this initiative, termed PROUCA, has already distributed hundreds of low-cost laptops for educational purposes in many Brazilian schools. However in spite of the numerous studies conducted in the country since PROUCA was launched, Brazil shows a lack of proficiency in basic information crucial for managing and improving any OLPC initiative (e.g., number of effectively used laptops, use time and distribution per subject, use location and school performance of users, and others). Therefore, the focus of this article is to introduce MEMORE, a computational environment for longitudinal on-line data collection, integration and an analysis of how PROUCA laptops are used by schools. Technical details about MEMORE's architecture, database and functional models are supplied and the results from real data collected from Brazilian public schools are presented and analyzed. They elucidate how MEMORE can be a valuable management tool in OLPC contexts.
In this paper, we take a new look at the problem of analyzing course evaluations. We examine ten years of undergraduate course evaluations from a large Engineering faculty. To the best of our knowledge, our data set is an order of magnitude larger than those used by previous work on this topic, at over 250,000 student evaluations of over 5,000 courses taught by over 2,000 distinct instructors. We build linear regression models to study the factors affecting course and instructor appraisals, and we perform a novel information-theoretic study to determine when some classmates rate a course and/or its instructor highly but others poorly. In addition to confirming the results of previous regression studies, we report a number of new observations that can help improve teaching and course quality.
In programming courses there are various ways in which students attempt to cheat. The most commonly used method is copying source code from other students and making minimal changes in it, like renaming variable names. Several tools like Sherlock, JPlag and Moss have been devised to detect source code plagiarism. However, for larger student assignments and projects that involve a lot of source code files these tools are not so effective. Also, issues may occur when source code is given to students in class so they can copy it. In such cases these tools do not provide satisfying results and reports. In this study, we present an improved process model for plagiarism detection when multiple student files exist and allowed source code is present. In the research in this paper we use the Sherlock detection tool, although the presented process model can be combined with any plagiarism detection engine. The proposed model is tested on assignments in three courses in two subsequent academic years.
The aim of this study is to investigate perceptions of parents in Croatia towards advantages and disadvantages of computer use in general as well as their children's computer use and to reveal parents' concerns and opinions about digital technology (DT) education in kindergarten. The paper reports on research findings from one of the large public kindergartens in the capital city of Croatia. A total of 152 parents of the children aged 3 to 7 enrolled at this early childhood education institution filled in the survey. Results show that although being very well equipped with digital technology hardware at home (99% of surveyed parents owns a computer, tablet or smartphone), parents feel anxious and are not always willing to allow their children to use DT. Results of our survey reveal young children's ability to use DT, but they also show that mere possession of DT at home and enabling children to use computers does not guarantee development of computer literacy and/or information literacy skills.
Social networks are progressively being considered as an intense thought for learning. Particularly in the research area of Intelligent Tutoring Systems, they can create intuitive, versatile and customized e-learning systems which can advance the learning process by revealing the capacities and shortcomings of every learner and by customizing the correspondence by group profiling. In this paper, the primary idea is the affect recognition as an estimation of the group profiling process, given that the fact of knowing how individuals feel about specific points can be viewed as imperative for the improvement of the tutoring process. As a testbed for our research, we have built up a prototype system for recognizing the emotions of Facebook users. Users' emotions can be neutral, positive or negative. A feeling is frequently presented in unpretentious or complex ways in a status. On top of that, data assembled from Facebook regularly contain a considerable measure of noise. Indeed, the task of automatic affect recognition in online texts turns out to be more troublesome. Thus, a probabilistic approach of Rocchio classifier is utilized so that the learning process is assisted. Conclusively, the conducted experiments confirmed the usefulness of the described approach.