Bodong Chen

Schools as Knowledge-Building Organizations: Thirty Years of Design Research.

In this article we review the Knowledge-Building literature, unpacking its conceptual framework, principle-based pedagogy, distinctive features, and issues regarding scalability and sustainability. The Knowledge-Building goal is to reframe education as a knowledge-creating enterprise, engaging students from the earliest years of schooling. Despite a 30-year program of research and development and recognition that there is a close fit between Knowledge Building and efforts to meet knowledge society needs, Knowledge Building is frequently reinterpreted along the general lines of bringing constructivist learning into schooling rather than means to reframing education as a knowledge-creating enterprise. This article aims to clarify Knowledge-Building goals and to make the opportunities afforded by Knowledge Building more accessible.

Twitter Archeology of learning analytics and knowledge conferences

The goal of the present study was to uncover new insights about the learning analytics community by analyzing Twitter archives from the past four Learning Analytics and Knowledge (LAK) conferences. Through descriptive analysis, interaction network analysis, hashtag analysis, and topic modeling, we found: extended coverage of the community over the years; increasing interactions among its members regardless of peripheral and in-persistent participation; increasingly dense, connected and balanced social networks; and more and more diverse research topics. Detailed inspection of semantic topics uncovered insights complementary to the analysis of LAK publications in previous research.

Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse

ABSTRACT As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish “productive” threads of knowledge-building discourse. A database of Grade 1–6 knowledge-building discourse was first coded for the posts’ contribution types and discussion threads’ productivity. Two distinctive temporal analysis techniques – Lag-sequential Analysis (LsA) and Frequent Sequence Mining (FSM) – were subsequently applied to detecting sequential patterns among contribution types that distinguish productive threads. The findings of LsA indicated that threads that were characterized by mere opinion-giving did not achieve much progress, while threads having more transitions among questioning, obtaining information, working with information, and theorizing were more productive. FSM further uncovered from productive threads distinguishing frequent sequences involving sustained theorizing, integrated use of evidence, and problematization of proposed theories. Based on the significance of studying temporality in collaborative learning revealed in the study, we advocate for more analytics tapping into temporality of learning.

Putting temporal analytics into practice: the 5th international workshop on temporality in learning data

Interest in temporal analytics---analytics that probe temporal aspects of learning so as to gain insights into the processes through which learning occurs---continues to grow. The relationships of temporal patterns to learning outcomes is a central area of interest. However, while the literature on temporal analyses is developing, there has been less consideration of the methods by which temporal analyses might be translated to actionable insights and thus, put into use in educational practice. Emerging temporal analysis techniques present both theoretical and practical challenges for producing and interpreting results. Synergetic actions are needed in order to support practitioners.

It's about time: 4th international workshop on temporal analyses of learning data

Interest in analyses that probe the temporal aspects of learning continues to grow. The study of common and consequential sequences of events (such as learners accessing resources, interacting with other learners and engaging in self-regulatory activities) and how these are associated with learning outcomes, as well as the ways in which knowledge and skills grow or evolve over time are both core areas of interest. Learning analytics datasets are replete with fine-grained temporal data: click streams; chat logs; document edit histories (e.g. wikis, etherpads); motion tracking (e.g. eye-tracking, Microsoft Kinect), and so on. However, the emerging area of temporal analysis presents both technical and theoretical challenges in appropriating suitable techniques and interpreting results in the context of learning. The learning analytics community offers a productive focal ground for exploring and furthering efforts to address these challenges. This workshop, the fourth in a series on temporal analysis of learning, provides a focal point for analytics researchers to consider issues around and approaches to temporality in learning analytics.

Exploring the Digital Divide: The Use of Digital Technologies in Ontario Public Schools

Combining data from a school principal survey with student demographics and achievement data, the present study aimed to develop a much needed understanding of ICT usage in Ontario’s K-12 public schools. Results indicated equitable first-order access to technology for schools, early integration of ICT from the earliest grades, frequent application of ICT in teaching, and an enabling effect of ICT on additional access to learning resources and distance learning. However, challenges were also uncovered in building technology infrastructure for a small fraction of schools; ensuring home access for schools with lower family incomes, smaller size or from remote regions; and providing teachers with professional development for choosing online materials and adopting emerging ICT-enabled teaching practice. Furthermore, this study highlighted the importance of parent involvement in ICT usage and the potential beneficial linkage between ICT usage and student learning achievement.

Visualizing semantic space of online discourse: the knowledge forum case

This poster presents an early experimentation of applying topic modeling and visualization techniques to analyze online discourse. In particular, Latent Dirichlet Allocation was used to convert discourse into a high-dimensional semantic space. To explore meaningful visualizations of the space, Locally Linear Embedding was performed reducing it to two-dimensional. Further, Time Series Analysis was applied to track evolution of topics in the space. This work will lead to new analytic tools for collaborative learning.

Using differentiated feedback to improve performance in introductory statistics

ABSTRACT In this study, we applied the regulatory focus theory to design a feedback intervention to promote learning in an introductory statistics course at a Midwestern public university in the USA. In total, 67 students were randomly assigned to receive feedback that either fitted or not fitted their regulatory focus. Through the analysis of covariance, we found that students in the fit condition had better performance than the non-fit condition, and there was a significant interaction between the individual’s regulatory focus and the type of feedback received. Further analysis showed that this interaction was significant only for the middle-performing students, but not for the lower or higher achievers. Findings from this study have implications for feedback design in higher education classrooms.

Examining motivations and self-regulated learning strategies of returning MOOCs learners

The present study examines behavioral patterns, motivations, and self-regulated learning strategies of returning learners---a special learner subpopulation in massive open online courses (MOOCs). To this end, data were collected from a teacher professional development MOOC that has been offered for seven iterations during 2014--2016. Data analysis identified more than 15% of all registrants as returning learners. Findings from click log analysis identified possible motivations of re-enrollment including improving grades, refreshing theoretical understanding, and solving practical problems. Further analysis uncovered evidence of self-regulated learning strategies among returning learners. Taken together, this study contributes to ongoing inquiry into MOOCs learning pathways, informs future MOOC design, and sheds light on the exploration of MOOCs as a viable option for teacher professional development.

Visualizing semantic space of online discourse

This poster presents an early experimentation of applying topic modeling and visualization techniques to analyze online discourse. In particular, Latent Dirichlet Allocation was used to convert discourse into a high-dimensional semantic space. To explore meaningful visualizations of the space, Locally Linear Embedding was performed reducing it to two-dimensional. Further, Time Series Analysis was applied to track evolution of topics in the space. This work will lead to new analytic tools for collaborative learning.