The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at ...understanding students’ learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students’ participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension.
Although there is a wealth of research focusing on PBL, most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students. There is little ...research that has studied interactivity in online PBL settings through the lens of Social Network Analysis (SNA) to explore both student and teacher factors that could help monitor and possibly proactively support PBL groups. This study adopts SNA to investigate how groups, tutors and individual student's interactivity variables correlate with group performance and whether the interactivity variables could be used to predict group performance.
We do so by analyzing 60 groups' work in 12 courses in dental education (598 students). The interaction data were extracted from a Moodle-based online learning platform to construct the aggregate networks of each group. SNA variables were calculated at the group level, students' level and tutor's level. We then performed correlation tests and multiple regression analysis using SNA measures and performance data.
The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity). A more dominating role for teachers may be a negative sign of group performance. Finally, a stepwise multiple regression test demonstrated that SNA centrality measures could be used to predict group performance. A significant equation was found, F (4, 55) = 49.1, p < 0.01, with an R2 of 0.76. Tutor Eigen centrality, user count, and centralization outdegree were all statistically significant and negative. However, reciprocity in the group was a positive predictor of group improvement.
The findings of this study emphasized the importance of interactions, equal participation and inclusion of all group members, and reciprocity and group cohesion as predictors of a functioning group. Furthermore, SNA could be used to monitor online PBL groups, identify important quantitative data that helps predict and potentially support groups to function and co-regulate, which would improve the outcome of interacting groups in PBL. The information offered by SNA requires relatively little effort to analyze and could help educators get valuable insights about their groups and individual collaborators.
Abstract
Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive ...interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group’s robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.
Universities are facing new challenges that pose various demands for developing learning environments. These challenges are related to different pedagogical approaches, the use of information and ...communications technology (ICT), the diversification of student populations, and new expectations related to working life. This study focused on university students’ perceptions of preferred learning environments and their thoughts about the best learning environments for the higher-education level. The main data consisted of answers to open questions from 230 students. Additionally, a questionnaire was used to gain an overall picture of students’ experiences in the current learning environment. Five main themes emerged: characteristics of the campus; available resources; flexibility of learning opportunities; pedagogy; and implementation of ICT in education. Further, two larger perspectives were highlighted. One was the need for informal learning environments (where students can study alone or with peers or just hang out). The second perspective pertains to the flexibility of learning, demonstrating the need for learning environments that allow participation without the need to come to the campus for face-to-face meetings. The need for resources (particularly ICT and the support and availability of teaching personnel) was also highlighted. The study provides important perspectives for developing appropriate learning environments for higher education.
Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields. Machine ...learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning and AI in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.
The rise of image-generating artificial intelligence (AI) tools has triggered changes in digital art and graphic design, provoking debates in the creative industry. However, scant research exists ...about children’s and youths’ insights into and encounters with generative AI. Building on sociocultural and new materialist perspectives, this exploratory study proposed to address this gap by exploring middle schoolers’ (
N
= 10) creative interaction with generative AI, particularly with text-to-image generative models. Qualitative content analyses of emerging learning activities evidenced how generative AI-formed relations were externalized through novel digital artefacts and collaborative discussions. Ideas evolved through peer collaboration organized around creative making with AI. Teachers facilitated relations between people and technology using dialogic teaching, providing room for unpredictability and critical reflection on the impacts of generative AI, especially authorship and copyright. The study concludes with a discussion of the potential uses of generative AI in future art education research and practice.
The integration of artificial intelligence (AI) topics into K–12 school curricula is a relatively new but crucial challenge faced by education systems worldwide. Attempts to address this challenge ...are hindered by a serious lack of curriculum materials and tools to aid teachers in teaching AI. This article introduces the theoretical foundations and design principles for implementing co-design projects in AI education, empirically tested in 12 Finnish classrooms. The article describes a project where 4th- and 7th-graders (N = 213) explored the basics of AI by creating their own AI-driven applications. Additionally, a framework for distributed scaffolding is presented, aiming to foster children's agency, understanding, creativity, and ethical awareness in the age of AI.
Machine learning (ML) literacy has recently been identified as one of critical skills students need to succeed as future creators and innovators. While the significance of introducing ML basics at ...kindergarten to twelfth grade (K-12) levels is increasingly acknowledged, there is limited research that focuses specifically on collaborative design of ML applications with middle school students. We posit that engaging young children to co-invent and make concrete prototypes improves their ideas, encourages them to become active participants, and allows them to establish the implications of the technology in their everyday lives. In order to lay the foundation for middle school ML education, we collaboratively designed and prototyped ML applications with 43 eighth grade students (ages 11 to 14) in a Nigerian school. The ideas generated by the students indicate that they began to identify the applicability of ML to their daily lives and as a solution to a plethora of societal challenges. This study provides learners' input and preliminary insights into approaches that could be adopted to promote ML within the compulsory level of education in an African setting. The research contributes to the limited body of knowledge available on effectively teaching ML to young learners using design-oriented pedagogy, especially in the context of an emerging country.
Artificial intelligence (AI) and the automation of creative work have received little attention in craft education. This study aimed to address this gap by exploring Finnish pre-service craft ...teachers' and teacher educators' (N = 15) insights into the potential benefits and challenges of AI, particularly text-to-image generative AI. This study implemented a hands-on workshop on creative making with text-to-image generative AI in order to stimulate discourses and capture imaginaries concerning generative AI. The results revealed that making with AI inspired teachers to consider the unique nature of crafts as well as the tensions and tradeoffs of adopting generative AI in craft practices. The teachers identified concerns in data-driven design, including algorithmic bias, copyright violations and black-boxing creativity, as well as in power relationships, hybrid influencing and behaviour engineering. The article concludes with a discussion of the complicated relationships the results uncovered between creative making and generative AI.
Researchers' efforts to build a knowledge base of how middle school students learn about machine learning (ML) is limited, particularly, considering the African context. Hence, we conducted an ...experimental classroom study (N = 32) within the context of extracurricular activities in a Nigerian middle school to discern how students engaged with ML activities. Furthermore, we explored whether participation in our intervention program elicit changes in students' ML comprehension, and perceptions. Using multiple qualitative data collection techniques including interviews, pre-post open-ended surveys and written assessments, we uncover evidence that indicated evolution of students’ ML understanding, ethical awareness, and societal implication of ML. In addition, our findings showed that a middle school student can learn and understand ML, even when one had no prior knowledge or interest in science related careers. The findings have implication for pedagogical design of AI instruction in middle school context. We discuss the implication of our results for researchers and relevant stakeholders, highlight the limitations and chart future work paths.
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