Exploring teachers' perceptions on different CSCL script editing tools

Despite the apparent maturity of the learning design field, and the variety of tooling available to support it, adoption among the teacher community (one of its alleged main targets) is still low. There is a lack of research on teachers' perception and use of different technological learning design tools, as existing evaluations are often restricted to a single tool. In order to explore whether there are common factors hampering teacher adoption, and which tool features might appeal to different teachers, more studies involving multiple authoring tools are needed. This paper provides a first step in this direction, describing a mixed methods study performed around a professional development workshop with 18 university teachers from multiple disciplines. This workshop exposed teachers to two different authoring tools (WebCollage and EDIT2), as they learned to create computer-supported collaborative learning (CSCL) designs and implement them. The findings of our interpretive study (which included questionnaires, observations, or group discussion recordings) support the idea that there is no single tool or set of features that are globally perceived as better, although our evidence also highlights certain factors as important for participant teachers – amongst others, the integration of learning designs with the ICT platforms for enactment, as well as with other tools that they already use in their everyday practice.     

Effects of prior knowledge on learning from different compositions of representations in a mobile learning environment

Two experiments examined the effects of prior knowledge on learning from different compositions of multiple representations in a mobile learning environment on plant leaf morphology for primary school students. Experiment 1 compared the learning effects of a mobile learning environment presenting text and photos of plants on a tablet PC, either in combination with or without real plants in the physical environment. Results indicated that there were no interactions between prior knowledge and experimental condition. Students who learned with tablet PCs only outperformed students who additionally learned with real plants on a comprehension and an application test. In addition, high prior knowledge students outperformed low prior knowledge students on both tests. To investigate whether these effects were caused by the specific characteristics of the combination of photos of real plants and real plants, Experiment 2 compared the differential effects of prior knowledge on learning with the combination of texts, photos and real plants to a combination in which the photos were replaced by schematic hand drawings. Results indicated that both low and high prior knowledge students, who learned with the combination of texts, schematic hand drawings and real plants performed better on a comprehension and an application test. High prior knowledge students performed better on both tests. It is concluded that the number and type of representations used is critical for the effectiveness of mobile learning environments.     

An exact algorithm with learning for the graph coloring problem

Given an undirected graph G=(V,E), the Graph Coloring Problem (GCP) consists in assigning a color to each vertex of the graph G in such a way that any two adjacent vertices are assigned different colors, and the number of different colors used is minimized. State-of-the-art algorithms generally deal with the explicit constraints in GCP: any two adjacent vertices should be assigned different colors, but do not specially deal with the implicit constraints between non-adjacent vertices implied by the explicit constraints. In this paper, we propose an exact algorithm with learning for GCP which exploits the implicit constraints using propositional logic. Our algorithm is compared with several exact algorithms among the best in the literature. The experimental results show that our algorithm outperforms other algorithms on many instances. Specifically, our algorithm allows to close the open DIMACS instance 4-Fullins_5.     

Direct measures of digital information processing and communication skills in primary education: Using item response theory for the development and validation of an ICT competence scale

In the past decade, several studies have measured ICT competences from the perspective of ICT self-efficacy. Such indirect measurements tend to have validity problems, as they depend on the pupils' ability to judge their own ICT competences. This study outlines the development of a performance-based digital test and the validation of a direct measure of ICT competence through the use of item response theory (IRT). More specifically, the test and the developed measure focus on primary-school pupils' proficiency in digital information processing and communication. 56 Items were administered to 560 pupils at the end of their primary-school education (age between 10.79 and 13.85 years old). The items were controlled for dimensionality, model-data fit, local item dependence and monotonicity. The final measure contains 27 items that refer to retrieving and processing digital information, and communication with a computer. The results indicate that the instrument is particularly reliable for low and median ability levels. Further refinement and possible future use of the instrument is discussed.     

The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning

Self-regulated learning is recognized as a critical factor for successful online learning, and students' perceived academic control and academic emotions are important antecedents of self-regulated learning. Because emotions and cognition are interrelated, investigating the joint relationship between perceived academic control and academic emotions on self-regulated learning would be valuable to understanding the process of self-regulated learning. Therefore, this study examined the role of academic emotions (enjoyment, anxiety, and boredom) in the relationship between perceived academic control and self-regulated learning in online learning. The path model was proposed to test the mediating and moderating effects of academic emotions. Data were collected from 426 Korean college students registered in online courses, and a path analysis was conducted. The results demonstrated that enjoyment mediated the relationship between perceived academic control and self-regulated learning, but the moderating effect of enjoyment was not significant. Boredom and anxiety did not have significant mediating effects on self-regulated learning, whereas they showed significant moderating effects in the relationship between perceived academic control and self-regulated learning. The role of academic emotions in learning and their implications for facilitating students' self-regulated learning in online learning were discussed based on the findings.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.