When learners collaborate on complex problems and open-ended tasks, the mechanism of negotiation plays a crucial role in establishing a common understanding and achieving a shared goal among them. Research has shown that negotiation improves problem-solving processes, making it an essential skill to be developed among learners. In this study, we propose a method for automating the identification of negotiation in learners' discourse during collaboration. We leverage language models like BERT, RoBERTa, and GPT2 along with traditional machine learning models like logistic regression to detect utterances of negotiation in learners' discourse while they collaboratively solve engineering estimation problem in an Open-Ended Learning Environment (OELE) called Modeling Based Estimation Learning Environment (MEttLE). Our findings suggest that our approach can accurately identify negotiation utterances with a high accuracy of 0.924 and 0.781 kappa value with a relatively smaller training set. Our method is the first step in real-time detection of negotiation, thereby enabling educators to design scaffolds and environments to help learners engage in effective negotiations.

The rapid rise of digital learning platforms has ushered in an era of educational transformation. While these platforms offer the advantage of scalability, they often fall short in facilitating meaningful interaction, which is pivotal for effective learning. Addressing this concern, our study introduces PyGuru 2.0, an innovative online learning environment for Python programming that aligns with the ICAP framework with an advanced conversational agent. We further investigate the interactions between students and a chatbot, employing a qualitative approach to comprehensively explore the diverse ways in which students interact with the chatbot. The interaction categories encompass a wide spectrum, including code assistance, error resolution, and conceptual explanation. In future, we plan to further elaborate on this coding scheme and see its impact on students’ learning outcomes.

Software conceptual design knowledge and skills are essential for graduating computer engineering students. Several approaches have been used to teach software conceptual design through various frameworks and learning environments. Our brief literature survey found that none of these systems analysed learner engagement with design elements using learning analytics. This study aims to analyse the learner engagement with design elements as they create software designs. We analysed log data of N=37 students in Think & Link, a technology-enhanced learning environment (TELE), based on the FBS design framework. The differences in student engagement are compared for two categories of learners, high and low, based on the learning gain in the pre and post-test performance. Differences and similarities in logs of actions were identified for high and low learners. Process Models were used to identify differences in the sequence of engagement with various design elements. We find high learners to identify significant design elements and create links among them. The analysis also provided insights on points for scaffolding to help low learners create meaningful designs.

In the past, unraveling learner interaction data in TELE was a challenge. However, the advent of LA has helped in uncovering latent information in log data to scaffold learning. This paper focuses on learner interaction in VeriSIM, a TELE, to teach software design diagrams. The learners’ performance in the system is used to categorize them into three groups, namely, "full scorers", "partial scorers", and "give uppers". Our analysis found that the full scorers spend a significantly higher duration per action than the give-uppers in an introductory challenge presented in the learning environment. Further analysis unravels the strategies used by consistent and inconsistent learners, and it was observed that the learner interaction strategies evolve with increasing difficulty levels as they navigate through the challenges.