Large-scale Learning Environments Research Articles

Learning Distributed Representations and Deep Embedded Clustering of Texts

Instructors face significant time and effort constraints when grading students’ assessments on a large scale. Clustering similar assessments is a unique and effective technique that has the potential to significantly reduce the workload of instructors in online and large-scale learning environments. By grouping together similar assessments, marking one assessment in a cluster can be scaled to other similar assessments, allowing for a more efficient and streamlined grading process. To address this issue, this paper focuses on text assessments and proposes a method for reducing the workload of instructors by clustering similar assessments. The proposed method involves the use of distributed representation to transform texts into vectors, and contrastive learning to improve the representation that distinguishes the differences among similar texts. The paper presents a general framework for clustering similar texts that includes label representation, K-means, and self-organization map algorithms, with the objective of improving clustering performance using Accuracy (ACC) and Normalized Mutual Information (NMI) metrics. The proposed framework is evaluated experimentally using two real datasets. The results show that self-organization maps and K-means algorithms with Pre-trained language models outperform label representation algorithms for different datasets.

Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

Diversity, equity, and inclusion (DEI) issues are urgent in education. We developed and evaluated a massive open online course ( N = 963) with embedded equity simulations that attempted to equip educators with equity teaching practices. Applying a structural topic model (STM)—a type of natural language processing (NLP)—we examined how participants with different equity attitudes responded in simulations. Over a sequence of four simulations, the simulation behavior of participants with less equitable beliefs converged to be more similar with the simulated behavior of participants with more equitable beliefs ( ES [effect size] = 1.08 SD). This finding was corroborated by overall changes in equity mindsets ( ES = 0.88 SD) and changed in self-reported equity-promoting practices ( ES = 0.32 SD). Digital simulations when combined with NLP offer a compelling approach to both teaching about DEI topics and formatively assessing learner behavior in large-scale learning environments.

Visions of CSCL: eight provocations for the future of the field

The field of CSCL is at a critical moment in its development. Internally we face issues of fragmentation and questions about what progress is being made. Externally the rise of social media and a variety of research communities that study the interactions within it raise questions about our unique identity and larger impact on the world. To illuminate the complex issues involved and the multiple perspectives that exist on them, we conducted an iterative and generative consultation with members of the CSCL community through individual interviews and public interactive presentations. The result is a series of eight provocations for the field, each presented as a dialogue between the Provocateur/Provocatrice (who seeks to shake up the status quo) and the Conciliator (who seeks to build on the achievements of our current traditions). The provocations address the debated need for six things: one conceptual framework to unite our diverse tools and theories (#1), prioritization of learner agency over collaborative scripting (#2), scrupulous scrutiny of when “collaboration” and “community” are said to exist (#3), the pursuit of computational approaches to understand collaborative learning (#5), learning analytics and adaptive support to be a top priority in the field (#6), and the expansion of our focus to seriously address social media and large-scale learning environments (#7). In addition, the provocations highlight two areas in which perhaps we should desist: the attempt to reconcile analytical and interpretative approaches to understanding collaboration (#4), and the goal of achieving tangible change in the education system (#8). There are no resolutions offered in this paper; the interchanges presented are designed to lay out the complex constellation of issues involved and can be considered a dialogue that we are still in the process of having with ourselves as individuals and together as a community. We stress the urgency and importance for the field of CSCL to take up these questions and tensions, and critically, to work towards decisions and resultant actions. Our future as a scientific community — our very existence and identity, depends on it.

Example-based feedback provision using structured solution spaces

Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain knowledge in order to provide feedback to learners adaptively to their needs. This approach implies two general drawbacks: the formalisation of a domain-specific model usually requires a huge effort, and in some domains it is not possible at all. In this paper, we propose feedback provision strategies in absence of a formalised domain model, motivated by example-based learning approaches. We demonstrate the feasibility and effectiveness of these strategies in several studies with experts and students. We discuss how, in a set of solutions, appropriate examples can be automatically identified and assigned to given student solutions via machine learning techniques in conjunction with an underlying dissimilarity metric. The plausibility of such an automatic selection is evaluated in an expert survey, while possible choices for domain-agnostic dissimilarity measures are tested in the context of real solution sets of Java programs. The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.

Similarity and difference: Student cooperation in Taiwanese and Australian science classrooms

AbstractIn this study, we examine the way in which students cooperate in Taiwanese and Australian science classrooms. We adopt the position that student cooperation is best understood by examining the patterns of variation within and between countries rather than trying to describe similarities and differences in essential terms. A critical analysis of large‐scale learning environment questionnaire data combined with in‐depth interview and observational data leads to several findings about the nature of student cooperation in the two countries. We conclude that students from Taiwan and Australia have a range of understandings and interpretations about what it means to cooperate in science classrooms. There are complex connections between cooperative behavior, student academic ability, sex, and nationality, which are best understood in socio‐cultural terms. © 2001 John Wiley & Sons, Inc. Sci Ed 85:694–711, 2001.

A Skeptical Perspective on the Adequacy Conception

Although this article is sympathetic to the philosophic goal of developing a conception of adequacy, it is skeptical as to whether this goal is feasible in the near future for anything but the very earliest primary grade levels (K-2). The problem is that the reform ideas suggested as the basis for delivering adequate instruction are really a grab bag of untested, nonoperationalized ideas. Indeed, little substantive progress has been made in designing effective large-scale learning environments for educationally disadvantaged students during the past 30 years. Experience from the Higher Order Thinking Skills (HOTS) project, a successful large-scale intervention for educationally disadvantaged students, is used to indicate just how difficult it is to develop actual, effective large-scale learning environments. This experience is also used to indicate why it is unlikely that the kinds of interventions that have the potential to consistently deliver adequate education will emerge in the next decade despite the brave reform rhetoric. At the same tine, a focus on adequacy is important for focusing people's attention on the need for more powerful learning environments.