Misconceptions are the “the old, the bad, and the ugly” prior knowledge, ideas or conceptions that the learners have that hinder their further learning in science. Several types of misconceptions that undergraduate students hold about reaction coordinate diagrams (from here on we use the term “reaction coordinate diagrams” and “energy diagrams” interchangeably) are described herein. The rationale of 223 students were coded based on their responses to a multiple-choice question on the topic, and interviews (n = 10) were used to delve deeper into the students’ knowledge structures. The results of the open coding of the rationale and the interviews were used in developing an instrument which was administered to 57 students. In this paper, we present the assessment instrument and the alternate conceptions that students have regarding energy diagrams that have not been reported in the literature yet. Implications for instructional approaches particular to the energy diagram topic are discussed.

Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a high-resolution picture of the activity that takes place during these student–computer interactions and explore whether the sequence of movements has an effect on learning. We present a use-case modelling of temporal data in an interactive learning environment with hand gestures, and discuss some validity threats if temporal dependencies are not accounted for. In particular, we assess how, if ignored, the temporal dependencies in the measurement of hand gestures might affect the goodness of fit of the statistical model and would affect the measurement of the similarity between elicited and enacted movement. Our findings show that accounting for temporality is crucial for finding a meaningful fit to the data. In using temporal analytics, we are able to create a high-resolution picture of how sensorimotor coordination correlates with learning gains in our learning system.

Despite many millions of dollars being spent each year to support undergraduate research experiences for students in the sciences, there has been little in the way of solid evaluation of these experiences. Recently, research has surfaced that addresses this issue and provides tools that are useful for researchers and evaluators to assess the gains students make from participation. This paper offers a new tool that can be used to measure student gains as a result of participation in these experiences. We discuss the development of the tool and use confirmatory factor analysis to evaluate the quality of the tool for measuring change over time in student confidence.