270 research outputs found
Utilizing Online Activity Data to Improve Face-to-Face Collaborative Learning in Technology-Enhanced Learning Environments
ķģė
¼ė¬ø (ė°ģ¬)-- ģģøėķźµ ėķģ : ģµķ©ź³¼ķźø°ģ ėķģ ģµķ©ź³¼ķė¶(ėģ§ķøģ ė³“ģµķ©ģ ź³µ), 2019. 2. Rhee, Wonjong .We live in a flood of information and face more and more complex problems that are difficult to be solved by a single individual. Collaboration with others is necessary to solve these problems. In educational practice, this leads to more attention on collaborative learning. Collaborative learning is a problem-solving process where students learn and work together with other peers to accomplish shared tasks. Through this group-based learning, students can develop collaborative problem-solving skills and improve the core competencies such as communication skills. However, there are many issues for collaborative learning to succeed, especially in a face-to-face learning environment. For example, group formation, the first step to design successful collaborative learning, requires a lot of time and effort. In addition, it is difficult for a small number of instructors to manage a large number of student groups when trying to monitor and support their learning process. These issues can amount hindrance to the effectiveness of face-to-face collaborative learning.
The purpose of this dissertation is to enhance the effectiveness of face-to-face collaborative learning with online activity data. First, online activity data is explored to find whether it can capture relevant student characteristics for group formation. If meaningful characteristics can be captured from the data, the entire group formation process can be performed more efficiently because the task can be automated. Second, learning analytics dashboards are implemented to provide adaptive support during a class. The dashboards system would monitor each group's collaboration status by utilizing online activity data that is collected during class in real-time, and provide adaptive feedback according to the status. Lastly, a predictive model is built to detect at-risk groups by utilizing the online activity data. The model is trained based on various features that represent important learning behaviors of a collaboration group.
The results reveal that online activity data can be utilized to address some of the issues we have in face-to-face collaborative learning. Student characteristics captured from the online activity data determined important group characteristics that significantly influenced group achievement. This indicates that student groups can be formed efficiently by utilizing the online activity data. In addition, the adaptive support provided by learning analytics dashboards significantly improved group process as well as achievement. Because the data allowed the dashboards system to monitor current learning status, appropriate feedback could be provided accordingly. This led to an improvement of both learning process and outcome. Finally, the predictive model could detect at-risk groups with high accuracy during the class. The random forest algorithm revealed important learning behaviors of a collaboration group that instructors should pay more attention to. The findings indicate that the online activity data can be utilized to address practical issues of face-to-face collaborative learning and to improve the group-based learning where the data is available.
Based on the investigation results, this dissertation makes contributions to learning analytics research and face-to-face collaborative learning in technology-enhanced learning environments. First, it can provide a concrete case study and a guide for future research that may take a learning analytics approach and utilize student activity data. Second, it adds a research endeavor to address challenges in face-to-face collaborative learning, which can lead to substantial enhancement of learning in educational practice. Third, it suggests interdisciplinary problem-solving approaches that can be applied to the real classroom context where online activity data is increasingly available with advanced technologies.Abstract i
Chapter 1. Introduction ļ¼
1.1. Motivation ļ¼
1.2. Research questions ļ¼
1.3. Organization ļ¼
Chapter 2. Background ļ¼
2.1. Learning analytics ļ¼
2.2. Collaborative learning ļ¼ļ¼
2.3. Technology-enhanced learning environment ļ¼ļ¼
Chapter 3. Heterogeneous group formation with online activity data ļ¼ļ¼
3.1. Student characteristics for heterogeneous group formation ļ¼ļ¼
3.2. Method ļ¼ļ¼
3.3. Results ļ¼ļ¼
3.4. Discussion ļ¼ļ¼
3.5. Summary ļ¼ļ¼
Chapter 4. Real-time dashboard for adaptive feedback in face-to-face CSCL ļ¼ļ¼
4.1. Theoretical background ļ¼ļ¼
4.2. Dashboard characteristics ļ¼ļ¼
4.3. Evaluation of the dashboard ļ¼ļ¼
4.4. Discussion ļ¼ļ¼ļ¼
4.5. Summary ļ¼ļ¼ļ¼
Chapter 5. Real-time detection of at-risk groups in face-to-face CSCL ļ¼ļ¼ļ¼
5.1. Important learning behaviors of group in collaborative argumentation ļ¼ļ¼ļ¼
5.2. Method ļ¼ļ¼ļ¼
5.3. Model performance and influential features ļ¼ļ¼ļ¼
5.4. Discussion ļ¼ļ¼ļ¼
5.5. Summary ļ¼ļ¼ļ¼
Chapter 6. Conclusion ļ¼ļ¼ļ¼
Bibliography ļ¼ļ¼ļ¼Docto
Active Methodologies for the Promotion of Mathematical Learning
In recent years, the methodologies of teaching have been in a process of transition. Multiple active methodologies have proliferated, with the aim of changing the concept we have had of teaching so far. These advocate for a student who plays a leading role in the process of building learning, while the teacher acts as a figure who facilitates and glimpses the paths to learning. In order to be able to carry out this type of teaching in an optimal way, it is necessary for the teaching and research community to be correctly trained in its pedagogical principles and in the tools that boost its implementation. Among these principles and tools, it is of vital importance that information and communication technologies (ICT) be adequately handled. The use of active methodologies (project-based learning, problem-based learning, service learning, flipped classroom, mobile learning, etc.) or innovative pedagogical approaches (simulation, role-playing, gamification, etc.) promotes an improvement in the motivation of students as well as their skills. This aspect is especially important in the area of mathematics, whose contents are characterized by their abstraction, thus highlighting the need for its dynamization in classrooms of different educational stages
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