We investigated the feasibility of automating the modeling of collaborative problem-solving skills encompassing both social and cognitive aspects. Leveraging a diverse array of cutting-edge techniques, including machine learning, deep learning, and large language models, we embarked on the classification of qualitatively coded interactions within groups. These groups were composed of four undergraduate students, each randomly assigned to tackle a decision-making challenge. Our dataset comprises contributions from 514 participants distributed across 129 groups. Employing a suite of prominent machine learning methods such as Random Forest, Support Vector Machines, Naive Bayes, Recurrent and Convolutional Neural Networks, BERT, and GPT-2 language models, we undertook the intricate task of classifying peer interactions. Notably, we introduced a novel task-based train-test split methodology, allowing us to assess classification performance independently of task-related context. This research carries significant implications for the learning analytics field by demonstrating the potential for automated modeling of collaborative problem-solving skills, offering new avenues for understanding and enhancing group learning dynamics.

Promoting diversity and equity in STEM requires ongoing assessment of progress in addressing gender disparities. Historically, women have faced challenges such as a lower sense of belonging and reduced persistence in STEM higher education. Prior research found robust correlations between a sense of belonging and STEM persistence and suggests intervention focusing on cultivating women's belonging to increase their persistence interest. We ask whether these gender gaps persist amongst motivated STEM learners, and whether we find men and women motivated by the same reasons in a global online learning community. We found that while women reported lower average sense of belonging to their field of study, their sense of identity with STEM is stronger than men. Our findings suggest no gender differences in persistence intent, but two key factors: sense of belonging and field identity were significant predictors of STEM persistence intent, with stronger coefficients from sense of belonging observed for both men and women. Notably, self-efficacy was an additional predictor of STEM intent only for women, even though women reported lower self-efficacy than men. These findings allow us to understand the nuances of different contributing factors to learners' interest in pursuing STEM. With such understanding, it allows us to more effectively strategize how to leverage AI in a scaled learning environment to promote equitable learning. We discussed the implications for fostering an equitable learning environment and supporting an international STEM community.

Abstract Ensembles of convolutional neural networks have shown remarkable results in learning discriminative semantic features for image classification tasks. However, the models in the ensemble often concentrate on similar regions in images. This work proposes a novel method that forces a set of base models to learn different features for a classification task. These models are combined in an ensemble to make a collective classification. The key finding is that by forcing the models to concentrate on different features, the classification accuracy is increased. To learn different feature concepts, a so-called feature distance loss is implemented on the feature maps. The experiments on benchmark convolutional neural networks (VGG16, ResNet, AlexNet), popular datasets (Cifar10, Cifar100, miniImageNet, NEU, BSD, TEX), and different training samples (3, 5, 10, 20, 50, 100 per class) show the effectiveness of the proposed feature loss. The proposed method outperforms classical ensemble versions of the base models. The Class Activation Maps explicitly prove the ability to learn different feature concepts. The code is available at: https://github.com/2Obe/Feature-Distance-Loss.git .

Collaborative problem-solving (CPS) is one of the most essential 21st century skills for success across educational and professional settings. The hidden-profile paradigm is one of the most prominent avenues of studying group decision making and underlying issues in information sharing. Previous research on the hidden-profile paradigm has primarily focused on static constructs (e.g., group size, group expertise), or on the information itself (whether certain pieces of information is being shared). In the current study, we propose a lens on individual and group’s collaborative problem-solving skills, to explore the relationships between dynamic discourse processes and decision making in a distributed information environment. Specifically, we sought to examine CPS skills in association with decision change and productive decision-making. Our results suggest that while sharing information has significantly positive association with decision change and effective decision-making, other aspects of social processes appear to be negatively correlated with these outcomes. Cognitive CPS skills, however, exhibit a strong positive relationship with making a (productive) change in students final decisions. We also find that these results are more pronounced at the group level, particularly with cognitive CPS skills. Our study shed lights on a more nuanced picture of how social and cognitive CPS interactions are related to effective information sharing and decision making in collaborative problem-solving interactions.

Question generation as a form of learnersourcing is both a metacognitive learning activity for students that encourages the development of higher-order thinking skills and a method for producing question banks and assessments. To better understand the motivations for learners who engage in learnersourcing and its impacts on student learning, we conducted an experiment that measured the effects of Multiple Choice Question (MCQ) generation in an introductory data science MOOC. We compared two approaches to question generation: (i) as a required activity, and (ii) as an optional activity. In both cases, the learnersourcing activity was part of the student summative evaluation. We found that learners value creating questions more, and create higher quality questions when they choose to do so compared to when it is required. At the same time there is a significant reduction in instructor evaluation workload in large-scale courses when learners engage by choice due to self-selection. Thus, we propose choice-based learnersourcing as a new form of scalable personalized learning design for MOOCs in particular. In addition, we contribute an exploration of the factors that influence learner choice to create (or not create) an MCQ, which can help contextualize the propensity of learners to engage in such learnersourcing activities.

There has been long-standing stereotypes on men and women's communication styles, such as men using more assertive or aggressive language and women showing more agreeableness and emotions in interactions. In the context of collaborative learning, male learners often believed to be more active participants while female learners are less engaged. To further explore gender differences in learners communication behavior and whether it has changed in the context of online synchronous collaboration, we examined students interactions at a sociocognitive level with a methodology called Group Communication Analysis (GCA). We found that there were no significant differences between men and women in the degree of participation. However, women exhibited significantly higher average social impact, responsivity and internal cohesion compared to men. We also compared the proportion of learners interaction profiles, and results suggest that women are more likely to be effective and cohesive communicators. We discussed implications of these findings for pedagogical practices to promote inclusivity and equity in collaborative learning online.