Utilizing Online Activity Data to Improve Face-to-Face Collaborative Learning in Technology-Enhanced Learning Environments

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 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 １ 1.1. Motivation １ 1.2. Research questions ４ 1.3. Organization ６ Chapter 2. Background ８ 2.1. Learning analytics ８ 2.2. Collaborative learning ２２ 2.3. Technology-enhanced learning environment ２７ Chapter 3. Heterogeneous group formation with online activity data ３５ 3.1. Student characteristics for heterogeneous group formation ３６ 3.2. Method ４１ 3.3. Results ５１ 3.4. Discussion ５９ 3.5. Summary ６４ Chapter 4. Real-time dashboard for adaptive feedback in face-to-face CSCL ６７ 4.1. Theoretical background ７０ 4.2. Dashboard characteristics ８１ 4.3. Evaluation of the dashboard ９４ 4.4. Discussion １０７ 4.5. Summary １１４ Chapter 5. Real-time detection of at-risk groups in face-to-face CSCL １１８ 5.1. Important learning behaviors of group in collaborative argumentation １１８ 5.2. Method １２０ 5.3. Model performance and influential features １２５ 5.4. Discussion １２９ 5.5. Summary １３２ Chapter 6. Conclusion １３４ Bibliography １４０Docto

Developing University Students’ Argumentative Discourse: An Ill-Structured Issue Pertaining To Black African Immigrants And African Americans

The overarching goal of this three-article five-chapter dissertation was to develop university students’ argument-counterargument integration abilities in persuasive essay writing on an ill-structured issue pertaining to black African immigrants and African Americans. Article One consisted of using phenomenography as a research approach to identify the qualitatively different ways university students perceive black African immigrants and African Americans. The university participants had 24 perceptions in which 10 pertained to black African immigrants and 14 to African Americans. The perceptions were grouped into six descriptive categories. The variations in perceptions were then used as statements for argumentation. The study implies that university students’ perceptions can be translated into arguments or claims to teach argumentation. Article Two is a mixed methods study that examined the effectiveness of criteria instruction and collaborative reasoning on university students’ argumentation abilities. The study consisted of 23 participants in the experimental group and 17 in the control. The following data were collected over the course of 10 weeks: participants\u27 pre-tests, mid-tests, post-tests, and final term papers; audio recordings of the collaborative reasoning group discussions; and observation notes. Analyses were done using a rubric, statistical tests, and dialogue types. The Mann-Whitney U test indicated that while there was no significant statistical difference between the experimental group and control group at the start of study (pre-test), there was a significant statistical difference between the groups on the mid-test, post-test, and final term paper. The findings indicate that the experimental group exhibited better argument-counterargument integration on the writing assessments as a result of learning the criteria instruction and participating in collaborative reasoning. A qualitative analysis revealed that mixed dialogue transpired in each collaborative reasoning group. The study implies that criteria instruction and collaborative reasoning can be used to develop university students’ argumentative discourse. Article Three is a case study that documented two first-year university students’ experiences in the learning of argument-counterargument integration in persuasive essay writing. Learning the criteria instruction for argumentation and participating in collaborative reasoning groups helped the case study university students (one) construct arguments using key elements specified in modified TAP, (two) discuss and explore the ill-structured issue with other university students, and (three) acquire information to develop their arguments and counterarguments. The study implies that educators meet with university students independently and recurrently to monitor students’ learning since paper analyzing is not enough to comprehend students’ knowledge and understanding of argumentation

From visualization to legal design : a collaborative and creative process

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Ethical Control of Unmanned Systems: lifesaving/lethal scenarios for naval operations

Prepared for: Raytheon Missiles & Defense under NCRADA-NPS-19-0227This research in Ethical Control of Unmanned Systems applies precepts of Network Optional Warfare (NOW) to develop a three-step Mission Execution Ontology (MEO) methodology for validating, simulating, and implementing mission orders for unmanned systems. First, mission orders are represented in ontologies that are understandable by humans and readable by machines. Next, the MEO is validated and tested for logical coherence using Semantic Web standards. The validated MEO is refined for implementation in simulation and visualization. This process is iterated until the MEO is ready for implementation. This methodology is applied to four Naval scenarios in order of increasing challenges that the operational environment and the adversary impose on the Human-Machine Team. The extent of challenge to Ethical Control in the scenarios is used to refine the MEO for the unmanned system. The research also considers Data-Centric Security and blockchain distributed ledger as enabling technologies for Ethical Control. Data-Centric Security is a combination of structured messaging, efficient compression, digital signature, and document encryption, in correct order, for round-trip messaging. Blockchain distributed ledger has potential to further add integrity measures for aggregated message sets, confirming receipt/response/sequencing without undetected message loss. When implemented, these technologies together form the end-to-end data security that ensures mutual trust and command authority in real-world operational environments—despite the potential presence of interfering network conditions, intermittent gaps, or potential opponent intercept. A coherent Ethical Control approach to command and control of unmanned systems is thus feasible. Therefore, this research concludes that maintaining human control of unmanned systems at long ranges of time-duration and distance, in denied, degraded, and deceptive environments, is possible through well-defined mission orders and data security technologies. Finally, as the human role remains essential in Ethical Control of unmanned systems, this research recommends the development of an unmanned system qualification process for Naval operations, as well as additional research prioritized based on urgency and impact.Raytheon Missiles & DefenseRaytheon Missiles & Defense (RMD).Approved for public release; distribution is unlimited

IS2020 A Competency Model for Undergraduate Programs in Information Systems: The Joint ACM/AIS IS2020 Task Force

The IS2020 report is the latest in a series of model curricula recommendations and guidelines for undergraduate degrees in Information Systems (IS). The report builds on the foundations developed in previous model curricula reports to develop a major revision of the model curriculum with the inclusion of significant new characteristics. Specifically, the IS2020 report does not directly prescribe a degree structure that targets a specific context or environment. Rather, the IS2020 report provides guidance regarding the core content of the curriculum that should be present but also provides flexibility to customize curricula according to local institutional needs