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Background: Online learning has recently surged due to the COVID‐19 global pandemic. Because of the pandemic, many universities were forced to move to online learning, and various online teaching and learning environments emerged, both asynchronous and synchronous. Objective: This study explores how a large university in the Southeastern United States of America converted an in‐person Lean Manufacturing professional course into synchronous online learning for industry participants. Method: The study analysed the performance of 212 in‐person and 43 online industry participants and examined the views of online participants about the training. Paired t‐tests, one‐way multivariate analysis of covariance (MANCOVA), and univariate analysis of covariance (ANCOVA) were utilized to evaluate the performance of the in‐person and online groups on the three competencies (Lean culture, problem‐solving and Lean systems). Results and Conclusion: Both online and in‐person programs performed equally well in two Lean Manufacturing competencies: Lean systems and problem solving. The online program outperformed the in‐person program in the third competency, Lean culture. Overall, the participants had a positive experience with the structure and organization of the online program, and their impression of the online learning environment for the program was positive. The study findings indicated that Lean Manufacturing could be trained effectively online and in person. Best practices are suggested for the conversion of in‐person to synchronous online engineering continuing education programs. Lay Description: What is already known about this topic: During the COVID‐19 pandemic, many in‐person lectures were converted to online lectures for undergraduate students, and some online lectures satisfied the students.Many researchers reported that the online participants yielded positive results, and the performance of online participants was comparable to that of in‐person participants.However, the participants mentioned that some of the challenges of online learning include poor concentration, internet losses and poor understanding of the use of technology. What this paper adds: A description of how in‐person training can be converted to online training for adult learners is a unique context since they bring experience to the learning process.A comparison of the performance of adult learners trained using in‐person performed compared to those trained in‐person. The implications of study findings for practitioners: The study's results indicated that both the online and the traditional in‐person programs yield almost similar results; thus, online education programs can be used when in‐person is impossible.Online education programs can be used when in‐person is impossible, and the best practice guidelines drawn from the study can benefit similar engineering continuing education programs. [ABSTRACT FROM AUTHOR]

Background: Despite its obvious relevance to computer science, computational thinking (CT) is transdisciplinary with the potential of impacting one's analytical ability. Although countless efforts have been invested across K‐12 education, there is a paucity of research at the postsecondary level about the extent to which CT can contribute to sustainable learning outcomes. Objectives: The current study examines how a series of Arduino‐based robotics learning activities capture the fuller essence of concepts related to CT. Methods: College students (n = 50) completed a series of six robotics learning activities. Think‐alouds, student reflections and performance scores were used to assess students' CT through a robotics challenge in virtual and physical learning environments. Results and Conclusions: Students verbalized CT concepts related to algorithmic thinking much more than abstraction, problem decomposition and testing and debugging. Students exposed to active learning performed better in a virtual robotics challenge compared to their peers in a traditional‐oriented classroom. Students' scores on the physical robotics challenge increased as a function of the number of references they made to CT concepts during the think‐alouds. It is possible to design pedagogical experiences that tap into various dimensions of CT at incremental levels of complexity through a series of Arduino‐based robotics activities. With the integration of an online simulation, students can visualize and transfer their CT skills between a virtual and physical learning environment, thus leading to more sustainable learning outcomes. Lay Description: What is already known about this topic: Computational thinking (CT) is the conceptual foundation for problem‐solving.CT shifted from unplugged to plugged activities.CT concepts can be fostered through educational robotics. What this paper adds: A multi‐dimensional approach to assessing CT.Applications of CT provide insights into skill transfer.An integrative theoretical approach to study the 'how' and the 'what' of CT. Implications for practice: Educational robotics activities can cater to a fuller range of CT skills.Virtual simulations with constructive feedback are beneficial for students in traditional settings.Mixed learning environments contribute to making links between lectures and labs. [ABSTRACT FROM AUTHOR]

Background: Collaborative problem solving (CPS) is important for success in the 21st century, especially for teamwork and communication in technology‐enhanced environments. Measurement of CPS skills has emerged as an essential aspect in educational assessment. Modern research in CPS relies on theory‐driven measurements that are usually carried out as manual annotations over recorded logs of collaborative activities. However, manual annotation has limited scalability and is not conductive towards CPS assessments at scale. Objective: We explore possibilities for automated annotation of actions in collaborative‐teams, especially chat messages. We evaluate two approaches that employ machine learning for automated classification of CPS events. Method: Data were collected from engineering, physics and electronics students' participation in a simulation‐based task on electronics concepts, in which participants communicated via text‐chat messages. All task activities were logged and time stamped. Data have been manually classified for the CPS skills, using an ontology that includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments. Results: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k‐nearest‐neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation. Implications: Our results suggest that a kNN classifier can be very effective for accurate annotation of CPS events. It achieves reasonably strong results even when trained on only half of the available data. This shows a promise towards reduction of manual data annotation for CPS measurement. Lay Description: What is already known about this topic: Collaborative problem solving (CPS) is important for success in the 21st century, especially when teamwork and communication occur in technology‐enhanced environments.Measurement of CPS skills requires classification and annotation of team interactions, in particular their communications, such as chat messages. What this paper adds: We explore possibilities for automated classification of CPS activities using machine learning approaches. Implications for practice: One of the approaches we explored is particularly effective and can help reduce the amount of manual annotations needed for CPS measurements. [ABSTRACT FROM AUTHOR]