Olusola O. Adesope

Remote laboratories (RL) have expanded access to food science laboratory education while shifting students into more autonomous learning environments. Self-Determination Theory (SDT) suggests that motivation in such settings depends on whether psychological needs, particularly competence, are satisfied as students navigate hands-on learning with reduced instructor presence. This cross-sectional, multi-institution study examined motivational profiles of food science students (n = 110) who completed take-home RL kits at six U.S. universities, using the Intrinsic Motivation Inventory. In a linear mixed model accounting for institutional clustering (n = 109), perceived competence, utility value, and effort investment were each uniquely and positively associated with interest; in the corresponding single-level model, the three constructs jointly accounted for 57% of the variance in interest. A comparison by prior food engineering coursework (n = 94) yielded a significant multivariate effect that no single construct accounted for. Discriminant function analysis showed that the groups differed on a contrast between perceived competence and interest: students without prior coursework reported competence exceeding their interest, whereas students with prior coursework reported interest matching or exceeding their competence (d = 0.79). This contrast held after adjustment for institution, although differences in the level of competence alone did not. Pre- and post-laboratory quizzes, which showed low reliability, indicated a small, non-significant gain in conceptual knowledge among the 68 students with matched scores. Findings provide multi-institution evidence that competence- and value-related processes covary with interest in autonomous, hands-on laboratory learning, and indicate that prior experience is associated with the balance between competence and interest rather than with the level of either.

Background: Virtual and remote laboratories (VRL) address accessibility and resource challenges in food science education, yet understanding motivational factors in technology-mediated learning remains critical. Guided by Situated Expectancy-Value Theory and Self-Determination Theory, this study examines motivation in VRL contexts where increased autonomy demand and reduced instructor presence may affect student engagement differently than traditional laboratories. This study examined relationships among perceived competence, effort, value, and interest in VRL experiences, and investigated whether prior knowledge moderates motivational profiles.Methods: Four low-cost food packaging laboratory modules (Water Vapor Transmission Rate, Intelligent Packaging, Puncture Resistance) were distributed to five U.S. universities. Data from 98 students completing pre- and post-lab assessments and motivational surveys were analyzed using multiple regression and multivariate analysis of variance.Results: Competence (β = .33, p < .001), value (β = .35, p < .001), and effort (β = .20, p = .022) significantly predicted interest, collectively explaining 55% of variance (F(3, 93) = 38.39, p < .001). Students without prior food engineering coursework reported significantly higher perceived competence than experienced students (d = .46, p = .032), while no differences emerged for value, effort, or interest.Conclusions: These findings underscore the importance of multi-dimensional motivational support in VRL design: competence-building scaffolding, value-enhancing connections to authentic practice, and effort-calibrated challenges. Results provide a framework for integrating motivationally supportive VRL into food science education while highlighting needs for future investigation of learning outcomes, longitudinal trajectories, and format-specific effects.

The increasing demand for a diverse pool of computing talent combined with a persistent shortage of skilled workers has engendered a need to support students pursuing computer science (CS) careers. Students often cite social isolation and lack of support as reasons for withdrawing from computing programs. This is especially true for those from underrepresented groups. One approach to combat these issues and to increase retention is to help students build communities of practice. This paper introduces the HELP Lab Emotional Learning Process Data (HELPd) Empathy Tool which was developed to generate emotional learning process (ELP) data-based interventions to help students build community. We intended to investigate the effectiveness of HELPd in supporting community building, but study participants did not proceed beyond self-reporting their emotions, which generated empathy nudges, only the first step in our scaffold for building community. As a result, our original study transformed into an empirical study of the design and usage of HELPd itself. Literature on affective learning and learning analytics provided guidance on the construction of HELPd. The underlying concept is that ELP data can be used to help students understand how both they and their peers feel during the programming process, allowing them to become more empathetic toward each other. Empathy, in turn, will lead to help-seeking and helpgiving, and these interactions will lead to community building. HELPd integrates a customized IDE to gather programming behavior data and a web application to collect ELP data. HELPd uses both sets of data to generate ELP data-based interventions. From this study, we learned that tools such as HELPd must be integrated into classroom coursework or students will not fully benefit from them. We recommend design changes to HELPd, including customizing interventions to engage populations from underrepresented groups and from groups with other demographics. With our recommended design changes and coursework integration, tools such as HELPd should help students to build community through help-seeking and help-giving actions.

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.

The Virtual Institutes for Cyber and Electromagnetic Spectrum Research and Employ Northwest Institute for Cybersecurity Education and Research was established to train U.S. cybersecurity talent through integrated experiential learning opportunities, including a multifaceted summer workshop featuring hands-on activities, field trips, and presentations.