The Effects of Three Instructor Participatory Roles on a Small Group’s Collaborative Concept Mapping

Collaborative concept mapping, as one of the widely used computer-supported collaborative learning (CSCL) modes, has been used to foster students’ meaning making, problem solving, and knowledge construction. Previous empirical research has used varied instructional scaffoldings and has reported different effects of those scaffoldings on collaboration. To further examine the effects of instructional scaffoldings, this research implements three different instructor participatory roles (i.e., cognitive contributor, group regulator, and social supporter) to support online collaborative concept mapping (CCM). We use multiple learning analytics methods to examine the group’s CCM processes from the social, cognitive, and metacognitive dimensions, supplemented with assessments of the concept maps. The research reveals different effects of three instructor participatory roles on the group’s collaborative behaviors, discourses, and performances. When the instructor engaged as a cognitive contributor, the student group achieved a lowly-interactive, low-level metacognitive engagement and behavior-oriented knowledge construction; when the instructor engaged as a group regulator, the student group achieved a socially-balanced, high-level metacognitive engagement and behavior-communication-interrelated knowledge construction; and when the instructor engaged as a social supporter, the student group achieved a highly-interactive, medium-level metacognitive engagement and communication-oriented knowledge construction. Based on the results, this research proposed pedagogical, analytical, and theoretical implications for future empirical research of CSCL.

Identifying the Structure of CSCL Conversations Using String Kernels

Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow by teachers due to the mixture of multiple and many times concurrent discussion threads, with different interaction patterns between participants. Automated guidance can be provided with the help of Natural Language Processing techniques that target the identification of topic mixtures and of semantic links between utterances in order to adequately observe the debate and continuation of ideas. This paper introduces a method for discovering such semantic links embedded within chat conversations using string kernels, word embeddings, and neural networks. Our approach was validated on two datasets and obtained state-of-the-art results on both. Trained on a relatively small set of conversations, our models relying on string kernels are very effective for detecting such semantic links with a matching accuracy larger than 50% and represent a better alternative to complex deep neural networks, frequently employed in various Natural Language Processing tasks where large datasets are available.

Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students’ diffusion-based centralities and a coded representation of their interactions in order to investigate the extent to which diffusion-based centralities are able to adequately capture information exchange and uptake processes. We performed a meta-analysis to pool the correlation coefficients between centralities and measures of academic achievement across all courses while considering the sample size of each course. Lastly, from a cluster analysis using students’ diffusion-based centralities aimed at discovering student role-taking within interactions, we investigated the validity of the discovered roles using the coded data. There was a statistically significant positive correlation that ranged from moderate to strong between diffusion-based centralities and the frequency of information sharing and argumentation utterances, confirming that diffusion-based centralities capture important aspects of information exchange and uptake. The results of the meta-analysis showed that diffusion-based centralities had the highest and most consistent combined correlation coefficients with academic achievement as well as the highest predictive intervals, thus demonstrating their advantage over traditional centrality measures. Characterizations of student roles based on diffusion centralities were validated using qualitative methods and were found to meaningfully relate to academic performance. Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students’ productive discussions and academic success.

LRDC: Learning Resource Diffusion Based on Credibility for Computer-Supported Collaborative Learning

Computer-supported collaborative learning (CSCL) is a learning strategy that gathers students together on campus through mobile application software on intelligent handheld devices to carry out creative exploration learning activities and social interaction learning activities. Learning resource diffusion is a very important constituent part of CSCL mobile software. However, learners will receive or forward a large number of learning resources such that short video, images, or short audio which will increase the energy consumption of forwarding nodes and reduce the message delivery success rate. How to improve the message delivery success rate is an urgent problem to be solved. To solve the aforementioned problem, this paper mainly studies the diffusion of learning resources in campus opportunistic networks based on credibility for CSCL. In campus opportunistic networks, learners who participate in collaborative learning can obtain the desired learning resources through the distribution and sharing of learning resources. Learning resource diffusion depends on the credibility of learners who participate in collaborative learning. However, the existing classical algorithms do not take into account the credibility between learners. Firstly, the concept of credibility in campus opportunistic networks is proposed, and the calculation method of credibility is also presented. Next, the problem of node initialization starvation is solved in this paper. The node initialization starvation phase of collaborative learning is defined and resolved in campus opportunistic networks. Based on the information of familiarity and activity between nodes formed in the process of continuous interaction, a learning resource diffusion mechanism based on node credibility is proposed. Finally, the paper proposes a complete learning resource diffusion algorithm based on credibility for computer-supported collaborative learning (LRDC for short) to improve the delivery success rate of learning resources on the campus. Extensive simulation results show that the average message diffusion success rate of LDRC is higher than that of classical algorithms such as DirectDeliver, Epidemic, FirstContact, and SprayAndWait under the different transmission speed, buffer size, and initial energy, which is averagely improved by 46.83%, 44.43%, and 45.6%, respectively. The scores of LRDC in other aspects are also significantly better than these classical algorithms.

Effect of computer-supported collaborative learning (CSCL) on students’ essay writing skills and their attitude toward e-learning

Computer-supported Collaborative Learning has been the focus of investigation in the field of foreign language learning for many years. This study aimed to investigate the impact of learning through blogging on the students’ essay writing skill and attitude. A total of 96 students from a Technological university were assigned to the study and were divided into 2 groups of experimental (bloggers) and control (regular) groups. They were instructed in two different methods by using different teaching methodologies. The bloggers, the experimental group students, did not receive any direct teaching; in fact, they received the lessons through weblogs. The findings showed that blogging had a significantly better effect on the learners’ writing skill improvement than the regular class. Also, a semi-structured interview was conducted to investigate the perceived usefulness, perceived ease of use, actual use of web and the students' attitude toward blogging, whose results demonstrated that the students’ attitudes and feelings can be reshaped as a result of exposure to e-learning. Keywords: Computer-supported, collaborative learning, blogging, e-learning, essay writing