Abstract. This paper presents a systematic literature review (SLR) about algorithms and programming teaching for beginner students in Brazilian higher education. Those courses are called CS1 (Computer Science 1) for short. The review was conducted on papers published in the following Brazilian conferences: SBIE, WIE, WEI and WAAAP between the years 2001 and 2014. This work searches for empirical evidences about the artefacts that influence success/failure rates in CS1 courses. Furthermore, the results of this review are compared to international results. From 394 papers selected 49 where analysed, but only 7 presented failure rates before and after experimentation. The failure rate on those papers was observed to be reduced from 45.6 to 32.6% after experimentation.Resumo. Este artigo apresenta uma revisão sistemática da literatura (RSL) sobre o ensino de programação e algoritmos para alunos iniciantes do ensino superior brasileiro, chamados CS1 (Computer Science 1). A RSL foi realizadà a partir da revisão dos artigos publicados nos eventos SBIE, WIE, WEI e WA-AAP entre os anos de 2001 a 2014. Este trabalho busca evidências empíricas dos artefatos que influenciam as taxas de sucesso/reprovação nos cursos CS1. Além disso, procurou-se comparar resultados desta revisão com os resultados apresentados internacionalmente. Do total de 394 artigos pré-selecionados, restaram 49, dos quais apenas 7 artigos apresentam taxas de reprovação de alunos antes e depois da realização do(s) experimento(s), reduzindo essa taxa, em média, de 45,6% para 32,6%.

In adaptive hypertexts the user is guided in two ways: through the existence of links and through link annotation or hiding. Link structures have been investigated, starting with Botafogo et al, and the effect of link annotation has been studied, for instance by Brusilovsky et al. This paper studies the combined effect of link structure and annotation/hiding on the navigation patterns of users. It defines empirical hubs and studies their correlation with hubs as defined by Kleinberg without considering adaptation. The data for the analysis have been extracted from the logs of the course "Hypermedia Structures and Systems," an online adaptive course offered at the Eindhoven University of Technology.

Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

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This paper motivates and describes GALE, the Generic Adaptation Language and Engine that came out of the GRAPPLE EU FP7 project. The main focus of the paper is the extensible nature of GALE. The purpose of this description is to illustrate how a single core adaptation engine can be used for different types of adaptation, applied to different types of information items and documents. We illustrate the adaptive functionality on some examples of hypermedia documents. In April 2012, David Smits defended the world's first adaptive PhD thesis on this topic. The thesis, available for download and direct adaptive access at http://gale.win.tue.nl/thesis/, shows that a single source of information can serve different audiences and at the same time also allows more freedom of navigation than is possible in any paper or static hypermedia document. The same can be done for course texts, hyperfiction, encyclopedia, museum, or other cultural heritage websites, etc. We explain how to add functionality to GALE if desired, to adapt the system's behavior to whatever the application requires. This stresses our main objective: to provide a technological base for adaptive (hypermedia) system researchers on which they can build extensions for the specific research they have in mind.

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Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.