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The retrospective is a crucial component of the agile software development process. In previous studies of retrospectives in undergraduate team software development projects, students exhibited limited and shallow reflection. We speculate that this is due to students' limited experience with reflection and the absence of clear guidance for engaging in deep reflection during agile retrospectives. To explore the potential for a pedagogical intervention to foster deeper reflection in retrospectives, we present an empirical comparison of a standard retrospective model against an enhanced retrospective model that scaffolds deeper levels of reflection by prompting students to justify and critique their practices and weigh alternative approaches. Through a systematic classification of the reflection level of statements made during individual brainstorming and team discussion phases of retrospectives, our study found that the enhanced model led to individuals and teams engaging in significantly higher levels of reflection. Our findings contribute to improving software engineering education by demonstrating the efficacy of an enhanced pedagogical model for team retrospectives. 

Not AActive, hands-on learning is essential for engineering education, fostering deep engagement and enhancing knowledge retention. This multi-institutional study investigates how different instructional methods—Hands-On, Virtual, and Lecture-only—combined with four distinct Low-Cost Desktop Learning Modules (LCDLMs: Hydraulic Loss, Double Pipe, Shell & Tube, and Venturi Meter) affect student engagement and learning outcomes. Anchored in the ICAP framework (Interactive, Constructive, Active, Passive), the study measured cognitive engagement through direct observations, virtual screen recordings, and self-reported surveys. It assessed learning gains using normalized pre- and post-tests among 2,316 undergraduate engineering students from eight universities. Results indicate that virtual instruction yields significantly higher learning gains, while the Shell & Tube module enhances active engagement through tangible, hands-on experiences. In contrast, the Hydraulic Loss module demonstrates the greatest impact on quantitative knowledge growth. These findings underscore the potential of integrating virtual simulations with physical learning tools to optimize instructional design in engineering education. Implications for future research include refining measurement instruments and exploring the long-term effects of hybrid instructional models. 

Over the past seven years, our team has disseminated low-cost hands-on learning hardware and associated worksheets in fluid mechanics and heat transfer to provide engineering students with an interactive learning experience. Previous studies have shown (1-5) the efficacy of teaching students with an active learning approach versus a more traditional lecture setup, with a number of approaches already available, such as simple active discussion, think-pair-share, flipped classrooms, etc. Our approach is differentiated by the inclusion of hardware to add both a visual aid and an opportunity for hands-on experimentation while keep the costs low enough for a classroom setting. Learning with a hands-on, interactive approach is supported by social cognitive theory (SCT) (6-7) and information processing theory (8). Unlike earlier views of learning theory, which simply posit that the key to learning is repetition, social cognitive theory considers the agency of the student and the social aspects of learning. The primary assumption of SCT is that students are active participants in the learning process, acquiring and displaying knowledge, skills, and behaviors that align with their goals through a process called triadic reciprocal causation, illustrated in figure 1. 

Misinformation around scientific issues is rampant on social media platforms, raising concerns among educators and science communicators. A variety of approaches have been explored to confront this growing threat to science literacy. For example, refutations have been used both proactively as warning labels and in attempts to inoculate against misconceptions, and retroactively to debunk misconceptions and rebut science denialism. Refutations have been used by policy makers and scientists when communicating with the general public, yet little is known about their effectiveness or consequences. Given the interest in refutational approaches, we conducted a comprehensive, pre-registered meta-analysis comparing the effect of refutation texts to non-refutation texts on individuals’ misconceptions about scientific information. We selected 71 articles (53 published and 18 unpublished) that described 76 studies, 111 samples, and 294 effect sizes. We also examined 26 moderators. Overall, our findings show a consistent and statistically significant advantage of refutation texts over non-refutation texts in controlled experiments confronting scientific misconceptions. We also found that moderators neither enhanced nor diminished the impact of the refutation texts. We discuss the implications of using refutations in formal and informal science learning contexts and in science communications from three theoretical perspectives. 

Assessing team software development projects is notoriously difficult and typically based on subjective metrics. To help make assessments more rigorous, we conducted an empirical study to explore relationships between subjective metrics based on peer and instructor assessments, and objective metrics based on GitHub and chat data. We studied 23 undergraduate software teams (n= 117 students) from two undergraduate computing courses at two North American research universities. We collected data on teams’ (a) commits and issues from their GitHub code repositories, (b) chat messages from their Slack and Microsoft Teams channels, (c) peer evaluation ratings from the CATME peer evaluation system, and (d) individual assignment grades from the courses. We derived metrics from (a) and (b) to measure both individual team members’contributionsto the team, and theequalityof team members’ contributions. We then performed Pearson analyses to identify correlations among the metrics, peer evaluation ratings, and individual grades. We found significant positive correlations between team members’ GitHub contributions, chat contributions, and peer evaluation ratings. In addition, the equality of teams’ GitHub contributions was positively correlated with teams’ average peer evaluation ratings and negatively correlated with the variance in those ratings. However, no such positive correlations were detected between the equality of teams’ chat contributions and their peer evaluation ratings. Our study extends previous research results by providing evidence that (a) team members’ chat contributions, like their GitHub contributions, are positively correlated with their peer evaluation ratings; (b) team members’ chat contributions are positively correlated with their GitHub contributions; and (c) the equality of team’ GitHub contributions is positively correlated with their peer evaluation ratings. These results lend further support to the idea that combining objective and subjective metrics can make the assessment of team software projects more comprehensive and rigorous.