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1. Reinforcing student learning by MATLAB simscape GUI program for introductory level mechanical vibrations and control theory courses

   https://doi.org/10.1177/03064190221085038  

   Pena, Paul; Utschig, Tristan; Tekes, Ayse (March 2022, International Journal of Mechanical Engineering Education)

   The highly mathematical nature of introductory level vibrations and control theory courses results in students struggling to understand the concepts. Hands-on activity demonstrated in class can help them better understand the concepts. However, there is still an ongoing effort to lower the currently substantial cost of educational laboratory equipment for undergraduate-level engineering courses. Also, with the COVID-19 crisis, the Spring 2020 academic year took an unexpected turn for academics and students all over the world. Engineering faculty who teach laboratories had to move online and instruct from home. Online course preparation takes more time and effort compared to traditionally designed face-to-face courses and was compounded considering the unprecedented situation where many instructors didn't have time to record data from existing lab equipment or record video in their laboratories. In this paper, we present a Matlab Simscape GUI program designed to simulate modeling and control of dynamical systems for vibrations and control theory courses, and their associated laboratories, as one potential solution for online instruction. To complement the simulation program, online classroom and homework activities were designed using a learning sciences approach connecting several critical educational theories which can bolster student motivation, engagement with the material, and overall learning performance. The simulation is presented along with data from 19 students who completed the associated classroom and homework activities. Survey results probing student perceptions about the value of the learning tasks for the simulation were overwhelmingly positive and indicate this approach holds promise in supporting student learning. 

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2. Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning

   Taub, M.; Azevedo, R. (December 2018, Journal of educational data mining)

   Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern mining and differential sequence mining to determine if there were sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most sequences of testing behaviors overall, specifically engaging in more sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data mining techniques for determining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning. 

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3. Patterns in student self-reported situational interest in online introductory geoscience labs during COVID

   https://doi.org/10.1080/10899995.2024.2359318  

   Bitting, Kelsey S; Ryker, Katherine; Teasdale, Rachel (June 2024, Journal of Geoscience Education)

   Triggered situational interest in introductory courses can encourage student engagement, motivation, and value for the geosciences. In-person labs have traditionally played a unique role in triggering situational interest compared to lectures, but the COVID transition online disrupted these dynamics. We examine students’ self-reported situational interest from 6,463 responses to weekly surveys in online introductory geoscience lab courses at five U.S. institutions during fall 2020 and spring 2021. Approximately half of students reported that labs were equally (49.4%) or more interesting (4.3%) online, compared to a hypothetical in-person option. Analysis showed a statistically-significant interaction between student situational interest and the combined effect of 1) the course the students were enrolled in and 2) the topic of the lab session (F (20, 6395) = 4.038, p < 0.001). However, topic and course together explain only about 4% of the variance in the dataset, indicating that other factors have a large role in triggering interest. Students who indicated that labs were less interesting online (46.3%) most often cited not being able to physically interact with instructional materials (56.3%) and difficulty interacting with peers (30.6%). When asked what revisions would increase their situational interest, additional hands-on interaction (22.8%) and increased relevance to their life or future career (20.2%) were the answer choices students selected most frequently. These findings identify modifications and enhancements grounded in students’ self-reported interest that can inform the design of online introductory geology labs. 

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4. Developing an Instrument to Measure Online Engineering Undergraduate Students’ Learning Experiences and Intentions to Persist

   Lee, E.; Brunhaver, S.; Bekki, J. (January 2020, Proceedings of the American Society for Engineering Education)

   The purpose of this study is to develop an instrument to measure student perceptions about the learning experiences in their online undergraduate engineering courses. Online education continues to grow broadly in higher education, but the movement toward acceptance and comprehensive utilization of online learning has generally been slower in engineering. Recently, however, there have been indicators that this could be changing. For example, ABET has accredited online undergraduate engineering degrees at Stony Brook University and Arizona State University (ASU), and an increasing number of other undergraduate engineering programs also offer online courses. During this period of transition in engineering education, further investigation about the online modality in the context of engineering education is needed, and survey instrumentation can support such investigations. The instrument presented in this paper is grounded in a Model for Online Course-level Persistence in Engineering (MOCPE), which was developed by our research team by combining two motivational frameworks used to study student persistence: the Expectancy x Value Theory of Achievement Motivation (EVT), and the ARCS model of motivational design. The initial MOCPE instrument contained 79 items related to students’ perceptions about the characteristics of their courses (i.e., the online learning management system, instructor practices, and peer support), expectancies of course success, course task values, perceived course difficulties, and intention to persist in the course. Evidence of validity and reliability was collected using a three-step process. First, we tested face and content validity of the instrument with experts in online engineering education and online undergraduate engineering students. Next, the survey was administered to the online undergraduate engineering student population at a large, Southwestern public university, and an exploratory factor analysis (EFA) was conducted on the responses. Lastly, evidence of reliability was obtained by computing the internal consistency of each resulting scale. The final instrument has seven scales with 67 items across 10 factors. The Cronbach alpha values for these scales range from 0.85 to 0.97. The full paper will provide complete details about the development and psychometric evaluation of the instrument, including evidence of and reliability. The instrument described in this paper will ultimately be used as part of a larger, National Science Foundation-funded project investigating the factors influencing online undergraduate engineering student persistence. It is currently being used in the context of this project to conduct a longitudinal study intended to understand the relationships between the experiences of online undergraduate engineering students in their courses and their intentions to persist in the course. We anticipate that the instrument will be of interest and use to other engineering education researchers who are also interested in studying the population of online students. 

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5. What Time is It? Student Modeling Needs to Know.

   Mao, Y.; Marwan, S.; Price, T.W.; Barnes, T.; & Chi, M. (January 2020, In Proceedings of the 13th International Conference on Educational Data Mining (EDM) 2020)

   null (Ed.)

   Abstract: Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and classic Markovian models such as Bayesian Knowledge Tracing (BKT) have been successfully applied for student modeling. However, much of this prior work is designed to handle sequences of events with discrete timesteps, rather than considering the continuous aspect of time. Given that time elapsed between successive elements in a student’s trajectory can vary from seconds to days, we applied a Timeaware LSTM (T-LSTM) to model the dynamics of student knowledge state in continuous time. We investigate the effectiveness of T-LSTM on two domains with very different characteristics. One involves an open-ended programming environment where students can self-pace their progress and T-LSTM is compared against LSTM, Recent Temporal Pattern Mining, and the classic Logistic Regression (LR) on the early prediction of student success; the other involves a classic tutor-driven intelligent tutoring system where the tutor scaffolds the student learning step by step and T-LSTM is compared with LSTM, LR, and BKT on the early prediction of student learning gains. Our results show that TLSTM significantly outperforms the other methods on the self-paced, open-ended programming environment; while on the tutor-driven ITS, it ties with LSTM and outperforms both LR and BKT. In other words, while time-irregularity exists in both datasets, T-LSTM works significantly better than other student models when the pace is driven by students. On the other hand, when such irregularity results from the tutor, T-LSTM was not superior to other models but its performance was not hurt either. 

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