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In recent years, many computerized test systems have been developed for diagnosing students' learning profiles. Nevertheless, it remains a challenging issue to find an adaptive testing algorithm to both shorten testing time and precisely diagnose the knowledge status of students. In order to find a suitable algorithm, four adaptive testing algorithms, based on ordering theory, item relational structure theory, Diagnosys, and domain experts, were evaluated based on the training sample size, prediction accuracy, and the use of test items by the simulation study with paper-based test data. Based on the results of simulation study, ordering theory has the best performance. An ordering-theory-based knowledge-structure-adaptive testing system was developed and evaluated. The results of this system showed that the two different interfaces, paper-based and computer-based, did not affect the examinees' performance. In addition, the effect of correct guessing was discussed, and two methods with adaptive testing algorithms were proposed to mitigate this effect. The experimental results showed that the proposed methods improve the effect of correct guessing. [ABSTRACT FROM AUTHOR]

In this paper we describe the results that we have obtained while integrating algorithm visualization (AV) movies (strongly tightened with the other teaching material), within a first-year undergraduate course on algorithms and data structures. Our experimental results seem to support the hypothesis that making these movies available significantly improved students' performances. Moreover, the movies were highly appreciated by the students (both from a comprehensibility point of view and from a usefulness point of view), even though with a low attitude towards the emerging video pod-cast technology. Finally, our results indicate the necessity of integrating the AV movies with audio comment, which seems to be one of the most interesting research question left open by our study. [ABSTRACT FROM AUTHOR]

In this paper, we describe an automatic personalization approach aiming to provide online automatic recommendations for active learners without requiring their explicit feedback. Recommended learning resources are computed based on the current learner's recent navigation history, as well as exploiting similarities and dissimilarities among learners' preferences and educational content. The proposed framework for building automatic recommendations in e-learning platforms is composed of two modules: an off-line module which preprocesses data to build learner and content models, and an online module which uses these models on-the-fly to recognize the students' needs and goals, and predict a recommendation list. Recommended learning objects are obtained by using a range of recommendation strategies based mainly on content based filtering and collaborative filtering approaches, each applied separately or in combination. [ABSTRACT FROM AUTHOR]

In this paper, two emerging learning and teaching methods have been studied: collaboration in concert with algorithm visualization. When visualizations have been employed in collaborative learning, collaboration introduces new challenges for the visualization tools. In addition, new theories are needed to guide the development and research of the visualization tools for collaborative learning. We present an empirical study, in which learning materials containing visualizations on different Extended Engagement Taxonomy levels were compared, when students were collaboratively learning concepts related to binary heap. In addition, the students' activities during the controlled experimental study were also recorded utilizing a screen capturing software. Pre- and post-tests were used as the test instruments in the experiment. No statistically significant differences were found in the post-test between the randomized groups. However, screen capturing and voice recording revealed that despite the randomization and instructions given to the students, not all of the students performed on the engagement level, to which they were assigned. By regrouping the students based on the monitored behavior, statistically significant differences were found in the total and pair average of the post-test scores. This confirms some of the hypothesis presented in the (Extended) Engagement Taxonomy. [ABSTRACT FROM AUTHOR]

In various research areas, the availability of open datasets is considered as key for research and application purposes. These datasets are used as benchmarks to develop new algorithms and to compare them to other algorithms in given settings. Finding such available datasets for experimentation can be a challenging task in technology enhanced learning, as there are various sources of data that have not been identified and documented exhaustively. In this paper, we provide such an analysis of datasets that can be used for research on learning and knowledge analytics. First, we present a framework for the analysis of educational datasets. Then, we analyze existing datasets along the dimensions of this framework and outline future challenges for the collection and sharing of educational datasets. [ABSTRACT FROM AUTHOR]

During the last years, development of open learning environments that support effectively their users has been a challenge for the research community of educational technologies. The open interactive nature of these environments results in users experiencing difficulties in coping with the plethora of available functions, especially during their initial efforts to use the system. In addition, -from the tutors' perspective- the problem solving strategies of the students are often particularly difficult to identify. In this paper, we argue that such problems could be tackled using machine learning techniques such as Bayesian Networks. We show how we can take advantage of log files obtained during field studies to build an adaptive help system providing the most useful support to the student, according to the state of interaction. On the other hand, we attempt to support the tutor, by automating the process of diagnosing students' problem solving strategies using Bayesian Networks. The presented approaches are discussed through examples of two prototypes that have been developed and corresponding evaluation studies. These studies have shown that the proposed approach can effectively support the tasks of students and tutors in such open learning environments. [ABSTRACT FROM AUTHOR]

Computer science provides with virtual laboratories, places where one can merge real experiments with the formalism of algorithms and mathematics and where, with the advent of multimedia, sounds and movies can also be added. In this paper we present a method, based on principles of formal ontology, allowing one to develop interactive educational tools. The structure of our system starts from general considerations on knowledge itself moving on to formal ontological principles in order to obtain a robust knowledge frame and good awareness of knowledge levels involved during the teaching process. Our system is split into object-knowledge - the knowledge of the phenomenon to be taught - and meta-knowledge - i.e., how to teach it. Using a trip (journey) metaphor, together with the flexibility of semantic graph representations, we define the reference frame and we apply it to a case study related to the Planetary Missions, the subject of our research. This tool is provided with a multimedia interface to show the results of several current missions , but may be implemented with new missions. [ABSTRACT FROM AUTHOR]

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are possible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study will evaluate and explain preprocessing and classification methods used to analyze teamwork dialogue from a dataset of chat data. Analytics methods evaluated in this study provide a direction for assessing the language of chat for teamwork dialogue and can help extend the work of technology enhanced language learning to not only focus on academic competency, but on the communication aspect too. [ABSTRACT FROM AUTHOR]

Concept maps are widely used in education, and have been acclaimed for their excellent results. For efficiently using concept maps in education, computer-based concept mapping systems have been developed. However these computer-based concept mapping systems are limited in their assessment algorithms. The assessment takes only concept nodes as the primary basis, with relation links playing only a minor role. To address this problem, this study proposes a new style of concept map, called the weighted concept map, which assigns a weight to each proposition in a concept map to represent its importance. This study proposes a new assessment based on a weighted concept map and diagnosis analysis. Two studies are conducted to evaluate the methods of assessment. [ABSTRACT FROM AUTHOR]