In essence, doctoral students who opt for an academic career path will have to become instructors. Nonetheless, research indicates that most PhD students have greater research experience than teaching experience. Furthermore, they may have taught through graduate teaching assistantships, which may or may not have included teaching-related training. If the doctoral students are not completely aware of all the different aspects that come with teaching, it can be challenging if they decide to become faculty members. The purpose of this study is to examine the factors influencing engineering doctoral students’ perceptions on their preparedness to teaching courses when they start their academic careers.A survey instrument was distributed digitally to students across 16 R1 universities across the United States (resulting n = 285). Three sections comprised the survey instrument: demographic data, free response questions, and Likert scale questions. The Likert scale questions evaluate the participants’ confidence or preparedness in areas of teaching such as the teaching and learning process (9 items); course design and delivery (8 items); creating a dynamic classroom (9 items); harnessing the power of technology (6 items); collaborative learning (6 items); and effective assessment (8 items). The data was collected in fall 2023. Exploratory factor analysis (EFA) was conducted to validate the factor structure. EFA revealed six factors, five factors were same as hypothesized (the teaching and learning process, course design and delivery, creating a dynamic classroom, collaborative learning, and effective assessment) and one new factor (ethical practices). The factor loadings for the final factors ranged from 0.42 to 0.99, and the internal consistency reliability (Cronbach’s α) for the six factors ranged from 0.77 to 0.86, indicating high reliability.In this study, the t-test, one-way ANOVA, and multiple regression analyses were conducted. Gender identity of engineering doctoral students significantly influenced only one factor (ethical practices). Men in comparison with other gender identities reported higher self-efficacy. None of the factors were significantly influenced by the engineering doctoral students’ perceptions on their preparedness to teach based on their first language. All six factors were influenced by the engineering doctoral students’ choice of career path. Overall, doctoral students interested in academia reported higher self-efficacy in the teaching-learning process in comparison with industry career path and other career paths. The multiple regression analysis revealed that career path (interested in academia), teaching assistant experience, and teaching experience, positive influence engineering doctoral students’ perceptions on their preparedness to teach.
Augmented Reality technology is increasingly being integrated into various fields, including surgery, to enhance intraoperative guidance and training. In the surgical domain, performing a successful surgery relies heavily on the expertise of the surgeon to navigate to the region of interest while avoiding critical structures. This study aims to develop and evaluate an Augmented Reality tool specifically designed for training in laparoscopic and minimally invasive surgeries, addressing the challenge of navigating to regions of interest skillfully. The procedure involved outlining the working principles for the Augmented Reality training tool, especially for minimally invasive surgeries. It was followed by a two-phase user study with a questionnaire to gather perspectives from clinical researchers, surgical trainees, and surgeon experts. Phase 1 of the study, involving a total of 36 participants, overlooked the perceived advantages of using Augmented Reality in surgeries and their present exposure to such tools. Phase 2, which involved 14 participants, specifically looked at the advantages of an augmented reality-based training tool and assessed its requirements. Subjective measures showed a positive expectant outlook among all subgroups to implement this tool in surgeries and surgical training. This is further validated by a significant p-value of less than 0.05 and chi-square value greater than 9.48 along with average Likert scale response being more than 4 in all the Likert-type of questions. Qualitative feedback from participants emphasized the tool's value in enhancing their training experience and providing real-time intraoperative support. In Phase 2 of the study, 78.6% of participants favored the semi-transparent color-coded edge-highlighted augmentation method, with 64.2% preferring Occlusion AR for vascular surgery training. A Chi-square test (p=0.0041) revealed statistically significant variability in responses, with 57.1% of participants rating the AR training tool's usefulness in future practice at 4, indicating broad agreement on its potential. Participants highlighted three main reasons for considering the AR tool most useful for training: exposure to complex or abnormal anatomy cases (71.4%), prevention of procedure-related complications (64.3%), and personalized preoperative planning based on patient variations (64.3%).The implications of these findings suggest that AR technology holds significant promise for improving surgical training and outcomes. Future work will focus on developing a robust AR training tool using the feedback and performing objective assessments with a mixed-participant trial group.
This study compared several machine learning algorithms to predict student programming learning performance in an online learning environment. It used Explainable Machine Learning (EML) techniques to enhance interpretability. A range of algorithms, including Random Forest, Extra Trees, CatBoost, XGBoost, Naive Bayes, and KNearest Neighbors (KNN), were compared, with Extra Trees delivering the best results. Distinct from other EDM research mainly focused on predictive efficiency, we contributed by using the EML technique of SHapley Additive Explanations (SHAP), rooted in the Game Theory framework, to enhance model interpretability at both global and individual levels. At the global level, summary plots showed overall feature impacts, bar plots quantified the average effect of each feature, and dependence plots highlighted specific relationships. At the individual level, force plots identified critical features for individual predictions, decision plots traced the cumulative impact of features from the base value to the final output, and waterfall plots provided a breakdown of predictions. This study contributes to EDM by offering accurate predictive models and detailed interpretability, helping educational stakeholders make data-informed decisions to improve student outcomes.
The Ministry of Education is the New Zealand Government's lead advisor on the New Zealand education system and plays a significant role in shaping an education system that delivers equitable and excellent outcomes for all learners. To this end, they provide a wide range of services, resources, guidance and advice to the education sector, families, and learners.In addition to these core functions, the Ministry also supports a number of software applications that are essential for school leaders and staff, education providers, and sector suppliers. The core functionality of these applications is to provide a secure communication platform for government education institutions and education providers to conduct daily operations, communicate with each other, manage, and deliver educational content. The main thrust of this paper is to analyse data pertaining to a selection of these applications. By understanding how these applications are used and by whom, the Ministry can optimise their performance and ensure they meet the needs of their users.There are two key research questions this paper aims to answer: (1) Are there any observable seasonal usage patterns? (2) Which key applications have the potential to find wider applicability outside of New Zealand? The seasonal usage patterns are useful for managing and adjusting server requirements, ensuring that resources are allocated efficiently and effectively throughout the year.To address these questions, the paper analyses user access data to various applications to gain insights into usage trends and behaviours. The analysis shows that most applications have user usage patterns that are consistent with New Zealand's seasonal trends, particularly in line with the seasonal pattern of New Zealand's education operations. This finding highlights the predictable nature of application usage, which can be leveraged to improve service delivery and resource allocation.Additionally, our insights are useful to other countries and regions that aim to build similar applications to support their own education sectors. Other education systems could leverage the insights gained from our usage patterns and consider the applicability of our applications in their systems.
