Social annotation is a collaborative learning practice that engages students in active reading, in-text commenting, and peer dialogue around shared digital texts. While it holds promise for supporting deep discourse and learning, challenges persist, including inconsistent, superficial, and fragmented participation. This study investigates how analytics-based suggestions influence students' social annotation engagement (defined multi-dimensionally as their contributions, peer interaction, and reading activity) in a fully asynchronous undergraduate course. Using a within-subject phased design over nine weeks, trace and textual data were collected from 94 students to analyze the patterns of their engagement across three phases: pre-intervention, intervention, and post-intervention. Quantitative analyses, including linear mixed-effects modeling and social network analysis, revealed that analytics-based suggestions significantly enhanced student engagement during the intervention phase: students produced more frequent, longer, and higher-quality annotation contributions, participated more actively and densely in peer discussions, and spent more time reading. However, these improvements were not sustained after the intervention ended, pointing to a reliance on the suggestions rather than the development of lasting practices. Findings highlight the potential of analytics-informed support to catalyze social annotation learning while underscoring the challenges for sustaining this engagement over time. The study contributes both empirical evidence and design considerations for integrating learning analytics into social annotation learning contexts in ways that promote deeper, more consistent, and sustained engagement.

This study fills a gap in knowledge regarding experienced instructors' use of learning analytics, focusing on differences in their approach, the knowledge and skills they activate, and the development of these knowledge and skills. Through a qualitative analysis of think-aloud interviews with 13 analytics-experienced instructors, two distinct profiles of analytics use emerged. Instructors in the first profile prioritized monitoring student engagement and performance to foster desirable behaviors, using analytics to align students with course expectations. Instructors in the second profile focused on understanding student perceptions of learning, aligning the course design with diverse learning behaviors and needs. To arrive at such use, instructors went beyond mere acquisition of technical knowledge to also integrate pedagogical knowledge into their analytics practices. Lastly, the study uncovered specific learning analytics supports, such as ongoing individual consultations, invaluable for developing the needed technical and pedagogical knowledge. Together, the results of this study reveal the pivotal role of pedagogy in analytics use, calling for refinement of conceptual models and tailoring of practical support for instructors.

Despite the growing interest in student-facing learning analytics, limited research has examined how students actually engage with these tools in practice. This study explores how students used message-based analytics sent via emails to support their engagement in collaborative annotation tasks in an online undergraduate statistics course over five weeks. Focusing on three key activities (analytic access, sensemaking, and action-taking), this study employed a mixed-methods approach, analyzing analytics access and action records from 91 students and think-aloud interviews with 10 students. Findings revealed consistently high access rates (i.e. opening an email that included analytics) across all weeks; however, more substantive engagement with the analytics varied based on alignment with students' learning routines and motivations for use. Students engaged in information-seeking based on curiosity about peer activity but noted a tension between their desire for concise data representations and the need for sufficient context to interpret and evaluate the information. Students did not always act immediately and directly on the analytics, but instead demonstrated indirect changes in their learning behaviors, such as heightened awareness and behavioral adjustments during later collaborative tasks. These insights inform a contextualized model of student analytics use, offering guidance for future analytics design and research and suggesting the need for an expanded notion of analytic actionability.

Since the inception of learning analytics, the CSCL community has been applying a range of methods to examine collaborative discourse, such as natural language processing and networked approaches. Despite significant advancements in methods, a critical gap persists in translating these data-driven insights into actionable strategies to inform pedagogical decisions. The proposed hybrid symposium seeks to bridge this gap by engaging learning scientists, CSCL researchers, and educators in discussions that integrate advanced discourse analytics with pedagogical design. Through five diverse presentations, the symposium will explore the integration of advanced discourse analysis techniques, such as networked approaches, multimodal analytics, and clustering analysis, into pedagogically meaningful designs and actionable pedagogical insights for collaborative learning environments. By fostering dialogue on the challenges and opportunities in contextualizing computational insights, this symposium aims to establish actionable approaches for enriching collaborative discourse and advancing the broader CSCL community’s theoretical, methodological, and practical understanding.

Social annotation has emerged as an important approach to supporting students’ social interaction and collaborative knowledge building in the classroom. Despite great interest among practitioners and a growing body of literature, social annotation activities are often guided by practical intuitions rather than informed by theories of learning and technology- supported collaboration. To strengthen social annotation practice, more work is needed to explore the systematic application of rich theories of learning and collaboration in this context. The proposed hybrid symposium aims to engage learning scientists, CSCL researchers, and stakeholders in productive dialogues to explore the integration of social annotation as a complex practice that can benefit from meaningful application of theories, explicit consideration of learning constructs, and careful design of technological and analytical support. The symposium will both contribute to social annotation practice in the classroom and help learning scientists and CSCL researchers in achieving broader impacts in the education system.

Actionability is a critical, but understudied, issue in learning analytics for driving impact on learning. This study investigated access and action-taking of 91 students in an online undergraduate statistics course who received analytics designed for actionability twice a week for five weeks in the semester. Findings showed high levels of access, but little direct action through the provided links. The major contribution of the study was the identification of unexpected indirect actions taken by students in response to the analytics which requires us to think (and look for evidence of impact) more broadly than has been done previously. The study also found that integrating analytics into existing learning tools and routines can increase access rates to the analytics, but may not guarantee meaningful engagement without better strategies to manage analytic timing. Together, this study advances an understanding of analytic actionability, calling for a broader examination of both direct and indirect actions within a larger learning ecosystem.

This study addresses the gap in knowledge about differences in how instructors use analytics to inform teaching by examining the ways that thirteen college instructors engaged with a set of university-provided analytics. Using multiple walk-through interviews with the instructors and qualitative inductive coding, two profiles of instructor analytics use were identified that were distinct from each other in terms of the goals of analytics use, how instructors made sense of and took actions upon the analytics, and the ways that ethical concerns were conceived. Specifically, one group of instructors used analytics to help students get aligned to and engaged in the course, whereas the other group used analytics to align the course to meet students' needs. Instructors in both profiles saw ethical questions as central to their learning analytics use, with instructors in one profile focusing on transparency and the other on student privacy and agency. These findings suggest the need to view analytics use as an integrated component of instructor teaching practices and envision complementary sets of technical and pedagogical support that can best facilitate the distinct activities aligned with each profile.

Team learning has become an essential activity in both professional and educational contexts. Learning analytics offers promising opportunities to support team learning by making information about group processes available to teams in real time to help them learn how to interact more productively. The design of team analytics requires three sets of critical decisions: the choices of metrics, the ways in which these metrics are presented, and how the analytics are implemented. This chapter describes four guiding principles for navigating this decision space in ways that produce relevant, understandable, and actionable analytics to support team learning.Application of the principles is illustrated in a design case of creating analytics for a discussion-based online course. The chapter concludes by describing the uptake of and student responses to the analytics in a pilot implementation and considering lessons learned for the design of team learning analytics.

Despite growing implementation of teacher-facing analytics in higher education, relatively little is known about the detailed processes through which instructors make sense of analytics in their teaching practices beyond their initial encounters with tools. This study unpacked the sensemaking process of thirteen instructors with analytic experience, using interviews that included walkthroughs of their analytics use. Qualitative inductive analysis was used to identify themes related to (1) the questions they asked of the analytics, (2) the techniques they used to interpret them, and (3) the challenges they encountered. Findings indicated that instructors went beyond a general curiosity to develop three types of questions of the analytics (goal-oriented, problem-oriented, and instruction modification questions). Instructors also used specific techniques to read and explain data by (a) developing expectations about the answers the analytics would provide, and (b) making comparisons to reveal student diversity, identify effects of instructional revision and diagnose issues. The study found instructors faced an initial learning curve when seeking and making use of relevant information, but also continued to revisit these challenges when they were not able to develop a routine of analytics use. These findings both contribute to a conceptual understanding of instructor analytic sensemaking and have practical implications for its systematic support.

This study describes a theory-informed application of data science methods to analyze the quality of reflections made in a health professions education program over time. One thousand five hundred reflections written by a cohort of 369 dental students over 4 years of academic study were evaluated for an overall measure of reflection depth (No, Shallow, Deep) and the presence of six theoretically-indicated elements of reflection quality (Description, Analysis, Feeling, Perspective, Evaluation, Outcome). Machine learning models were then built to automatically detect these qualities based on linguistic features in the reflections. Results showed a dramatic increase from No to Shallow reflections from the start to end of year one (20%  →  66%), but only a limited gradual rise in Deep reflections across all four years (2%  →  26%). The presence of all six reflection elements increased over time, but inclusion of Feelings and Analysis remained relatively low even at the end of year four (found in 44% and 60% of reflections respectively). Models were able to reliably detect the presence of Description (κTEST = 0.70) and Evaluation (κTEST = 0.65) in reflections; models to detect the presence of Analysis (κTEST = 0.50), Feelings (κTEST = 0.54), and Perspectives (κTEST = 0.53) showed moderate performance; the model to detect Outcomes suffered from overfitting (κTRAIN = 0.90, κTEST = 0.53). A classifier for overall depth built on the reflection elements showed moderate performance across all time periods (κTEST > 0.60) but relied almost exclusively on the presence of Description. Implications for the conceptualization of reflection quality and providing personalized learning support to help students develop reflective skills are discussed.

Reflection assessment is a critical component of health professions education that can be used for personalized learning support. However, reflection assessment at scale remains a challenge due to the demanding nature of tasks and the common use of simplified criteria of quality. This study addressed this issue by developing a multi-dimensional automated assessment that uses linguistic models to classify reflections by overall quality (depth) and the presence of six constituent elements denoting quality (description, analysis, feeling, perspective, evaluation, and outcome). 1500 reflections from 369 dental students were manually coded to establish ground truth. Classifiers for each of the six elements were trained and tested based on linguistic features extracted using the LIWC tool applying both single-label and multi-label classification approaches. Classifiers for depth were built both directly from linguistic features and based on the presence of the six elements. Results showed that linguistic modeling can be used to reliably detect the presence of reflection elements and the level of depth. However, the depth classifier showed a heavy reliance on cognitive elements (description, analysis, and evaluation) rather than the others. These findings indicate the feasibility of implementing multidimensional automated assessment in health professions education and the need to reconsider how quality of reflection is conceptualized.

The process of using analytic data to inform instructional decision-making is acknowledged to be complex; however, details of how it occurs in authentic teaching contexts have not been fully unpacked. This study investigated five university instructors’ use of a learning analytics dashboard to inform their teaching. The existing literature was synthesized to create a template for inquiry that guided interviews, and inductive qualitative analysis was used to identify salient emergent themes in how instructors 1) asked questions, 2) interpreted data, 3) took action, and 4) checked impact. Findings showed that instructors did not always come to analytics use with specific questions, but rather with general areas of curiosity. Questions additionally emerged and were refined through interaction with the analytics. Data interpretation involved two distinct activities, often along with affective reactions to data: reading data toidentify noteworthy patterns and explaining their importance in the course using contextual knowledge. Pedagogical responses to the analytics included whole-class scaffolding, targeted scaffolding, and revising course design, as well two new non-action responses: adopting a wait-and-see posture and engaging in deep reflection on pedagogy. Findings were synthesized into a model of instructor analytics use that offers useful categories of activities for future study and support

The purpose of this study was to develop team projects in design thinking, for promotion and examination with the cultivation of group creativity. Research was conducted during the spring of 2017, with sixteen graduate students. Using artifact-based interviews, we analyzed the development of group creativity during the five stages of design thinking: understanding knowledge, empathizing, sharing perspectives, generating ideas, and prototyping. Results showed that analytical thinking was present throughout the overall project, while factors related to group creativity (such as learner orientation, interpersonal understanding, and flexibility) were observed at different rates as the project progressed. Results suggest that such pedagogical strategies as idea checking and training for applicability are necessary in order to foster group creativity.

The purpose of this study was to investigate how to facilitate learners' engagement and persistence in massive open online courses (MOOCs). Specifically, this study used structural equation modeling to examine the structural relationships among academic self-efficacy, teaching presence, perceived usefulness, and perceived ease of use, learning engagement, and learning persistence in MOOCs. For the data analysis, we selected as the research subjects 306 learners who were taking MOOCs in South Korea. The results indicated that academic self-efficacy, teaching presence, and perceived usefulness had significant direct effects on learning engagement. Furthermore, teaching presence and perceived ease of use had direct effects on learning persistence. Finally, learning engagement had indirect effects on the relationships between academic self-efficacy, teaching presence, perceived usefulness, and learning persistence. These findings suggest implications for designing and developing effective instructional and learning strategies in MOOCs in terms of learners’ perceptions of themselves, instructors, and learning support systems.

본 연구는 국내 대학환경에서의 소셜러닝에 관한 최근 연구 동향을 분석하고, 소셜러닝의 효과성 및 교육적 시사점을 제안하는 것을 목적으로 한다. 이를 위해 최근 6년간 교육학 분야의 학술지에 게재된 소셜러닝 관련 논문 63편을 분석하였다. 구체적인 연구 결과는 다음과 같다. 첫째, 연구내용적 측면에서는 2010년 이후 소셜러닝 연구의 수가 꾸준히 증가하였으며, 가장 많은 빈도를 차지한 연구영역와 연구방법은 활용영역과 조사연구방법이었다. 둘째, SNS 활용적 측면과 관련하여 매체유형과 활용목적으로는 각각 페이스북과 형식적 소셜러닝(FSL)의 빈도가 가장 높았으며, SNS 활용 집단 크기의 경우 연구마다 목적에 따라 상이한 것으로 나타났다. 셋째, 소셜러닝의 효과성 측면에서 분석된 실험연구들은 소셜러닝이 학습성과 학습과정 학습자특성 변인에 있어 효과성을 향상시켰다고 밝혔으며, 조사연구들에서는 독립변인으로는 학습자 특성 관련 변인을, 종속변인으로는 만족도, 성취도, 참여도 변인을 주로 활용하였다. 본 연구는 대학교육환경에서 소셜러닝의 교육적 가치를 확인하고, 효과적인 소셜러닝 수행을 위한 기초 자료를 제공하였다는 점에서 의의를 가진다. The purpose of this study was to analyze domestic research trends of social learning in higher education, and find out educational implications with regard to the effectiveness of social learning. The 63 articles on social learning were finally analyzed, which were published in KCI journals. The results are as follows: Firstly, in respect of research contents, the research area of utilization and the survey methods were most frequently used in those studies. Secondly, as to the use of SNS, the analyzed studies were centralized on Facebook and Formal Structured Learning. Thirdly, as for the effectiveness of SNS, the experimental studies showed that social learning has an effective impact on the learning outcomes, learning processes, and learners' characteristics. In addition, survey studies most frequently set the independent variables as learners' characteristics and the dependent variables as participation, satisfaction, and academic achievement. This research has a significance in terms of verifying the educational implications of social learning, and providing the preliminary data to facilitate the performance for the effective social learning.

The purpose of this study is to investigate the effects of academic emotion regulation and group cohesiveness on learning satisfaction and learning interest in the flipped learning at the university. In order to examine the purpose, this study applied a problem-solving classroom model to the flipped learning. 33 university students participated in the flipped learning activities for 9 weeks, and the data from them were used for multiple regression analysis. The results indicated that learners’ academic emotion regulation did not affect learning satisfaction, and 교신저자 : 이정민(이화여자대학교) 논문투고 : 2016-06-15 논문심사 : 2016-06-15 심사완료 : 2016-07-15 Journal of The Korean Association of Information Education Vol. 20, No. 4, August 2016, pp. 341-356 http://dx.doi.org/10.14352/jkaie.2016.20.4.341 © 2016 KAIE 342 정보교육학회논문지 제20권 제4호 subjects’ group cohesiveness influenced on learning satisfaction. Second, learners’ academic emotion regulation did not affect learning interest, but their group cohesiveness had an impact on learning interest. This research has several implications with regard to suggesting the guidelines and conditions for the design and implementation of the flipped classroom at the university.