Video is becoming a dominant medium for the delivery of educational material. Despite the widespread use of video for learning, there is still a lack of understanding about how best to help people learn in this medium. This study demonstrates the use of thermal camera as compared to traditional self-reported methods for assessing learners' cognitive load while watching video lectures of different styles. We evaluated our approach in a study with 78 university students viewing two variants of short video lectures on two different topics. To incorporate subjective measures, the students reported on mental effort, interest, prior knowledge, confidence, and challenge. Moreover, through a physical slider device, the students could continuously report on their perceived level of difficulty. Lastly, we used thermal sensor as an additional indicator of students' level of difficulty and associated cognitive load. This was achieved through, continuous real-time monitoring of students by using a thermal imaging camera. This study aims to address the following: firstly, to analyze if video styles differ in terms of the associated cognitive load. Secondly, to assess the effects of cognitive load on learning outcomes; could an increase in the cognitive load be associated with poorer learning outcomes? Third, to see if there is a match between students' perceived difficulty levels and a biological indicator. The results suggest that thermal imaging could be an effective tool to assess learners' cognitive load, and an increased cognitive load could lead to poorer performance. Moreover, in terms of the lecture styles, the animated video lectures appear to be a better tool than the text-only lectures (in the content areas tested here). The results of this study may guide future works on effective video designs, especially those that consider the cognitive load.

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Abstract-In this paper, Engineering Education as a discipline has been analyzed by taking IEEE Transactions on Education (IEEE T Educ) as a case; for examining the various trends that have been emerging over time. Based on various criteria of authorship and citation, an effort is made to highlight the main contributors or top authors of this engineering education community. It was found that authorship trends have been shifting more towards collaboration. It was also found that the authorship community is growing, both in terms of publications and publishing authors. Study of citation patterns during the last decade, reveals a high citation count per article, which indicates a high readership of this journal. Later, the study of authorship and citation patterns shed light on the trend that multi-author articles are cited more often than single-author articles. This study was compared with earlier studies in the field of Engineering Education Research (EER) using keyword analysis and temporal evolution and distribution of keywords. Additionally key-phrase and topic modeling was performed to identify leading and evolving research areas within the EER., Analysis of word co-occurrence was performed to discover the main context in which the keywords have been used. Lastly, topic modeling techniques were applied for probabilistic distributions of IEEE topics and the results were in line with earlier studies.

This article researches the influence of IJEEE on electrical engineering and electrical engineering education as a discipline. For this purpose, the history of this journal has been presented from a citation perspective. To identify leading and evolving research areas within IJEEE the authors conducted keyword analysis, which additionally showed how IJEEE contains both educational and technical contributions. The authors also studied the temporal evolution and distribution of keywords. Word co-occurrence was analysed to discover the main context in which the keywords have been used. The analysis also revealed the prominent contributors within the community of IJEEE based on various authorship and citation criteria. It was observed that the influential authors appear in multiple ways, i.e. most of the authors who were influential by one criterion also made to the top list of other criteria. The authors concluded that the single-author pattern is quite prominent within this community, and very little work has been done between the same co-authors. Therefore, there is a need to encourage IJEEE authors to write more collaborative publications so that the authorship/co-authorship network may grow.

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Confusion is an important epistemic emotion because it can help students focus their attention and effort when solving complex learning tasks. However, unresolved confusion can be detrimental because it may result in students’ disengagement. This is especially concerning in simulation environments using discovery-based learning, which puts more of the onus for learning on the students. Thus, students with misconceptions may become confused. In this study, the possible moments of confusion in a simulation-based predict-observe-explain (POE) environment were investigated. Log-based interaction patterns of undergraduate students from a fully online course were analyzed. It was found that POE environments can offer a level of difficulty that potentially triggers some confusion, and a likely moment of students’ confusion was the observe task. It was also found that confidence in prior knowledge is an important factor that can contribute to students’ confusion. Students mostly struggled when they discovered a mismatch between the subjective and objective correctness of their responses. The effects of such a mismatch were more pronounced when confusion markers were analyzed than when students’ learning outcomes were observed. These findings may guide future works to bridge the knowledge gaps that lead to confusion in POE environments.

NSF and several private foundations fund his research. His research and teaching focuses on policy of P-12 engineering, how to support teachers and students' academic achievements through engineering learning, the measurement and support of change of habits of mind, particularly in regards to sustainability and the use of cyber-infrastructure to sensitively and resourcefully provide access to and support learning of complexity.

Task difficulty (TD) reflects students' subjective judgement on the complexity of a task. We examine the TDs data of 236 undergraduate students in a simulation-based Predict-Observe-Explain (POE) environment using three different labels easy, medium and hard. Generally, the students who perceive the tasks to be easy or hard perform poorly at the transfer task than the students who perceive the tasks to be medium or moderately difficult. Sequences of students' TDs are analysed which consist of a set of several judgements, collected once for each task in a POE sequence. The analysis suggests that given a sequence of TDs, difficulty level hard followed by a hard may lead to poorer learning outcomes at the transfer task. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes at the transfer task. In terms of the TD models, we identify student behaviours that can be reflective of their perceived difficulties. Generally, the students who report that the tasks are easy, adopt a trial-and-error behaviour where they spend lesser time and make more attempts on tasks. By comparison, the students who complete the tasks in a longer time by making more attempts are likely to report that the following task is hard. For the students who report medium TDs, mostly these students seem to reflect on tasks where they spend a long time and require fewer attempts for task completions. Additionally, these students provide longer texts for explaining their hypothesis reasoning. Understanding how student behaviours and TDs manifest over time and how they impact students' learning outcomes is useful, especially when designing for real-time educational interventions, where the difficulty of the tasks could be optimised for students. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximise students' learning.