Search Results (137)

This paper introduces CodeDive, a web-based programming environment with real-time behavioral tracking designed to enhance student progress assessment and provide timely support for learners, while also addressing the academic integrity challenges posed by Large Language Models (LLMs). Visibility into the student’s learning process has become essential for effective pedagogical analysis and personalized feedback, especially in the era where LLMs can generate complete solutions, making it difficult to truly assess student learning and ensure academic integrity based solely on the final outcome. CodeDive provides this process-level transparency by capturing fine-grained events, such as code edits, executions, and pauses, enabling instructors to gain actionable insights for timely student support, analyze learning trajectories, and effectively uphold academic integrity. It operates on a scalable Kubernetes-based cloud architecture, ensuring security and user isolation via containerization and SSO authentication. As a browser-accessible platform, it requires no local installation, simplifying deployment. The system produces a rich data stream of all interaction events for pedagogical analysis. In a Spring 2025 deployment in an Operating Systems course with approximately 100 students, CodeDive captured nearly 25,000 code snapshots and over 4000 execution events with a low overhead. The collected data powered an interactive dashboard visualizing each learner’s coding timeline, offering actionable insights for timely student support and a deeper understanding of their problem-solving strategies. By shifting evaluation from the final artifact to the developmental process, CodeDive offers a practical solution for comprehensively assessing student progress and verifying authentic learning in the LLM era. The successful deployment confirms that CodeDive is a stable and valuable tool for maintaining pedagogical transparency and integrity in modern classrooms. Full article

Active learning promises richer engagement, yet transnational English-medium engineering classrooms can remain quiet even when students are motivated. This study aims to explain this silence by examining the factors that encourage students to participate, the barriers that discourage them, and how student characteristics and coping strategies influence their participation. We conducted a mixed-methods survey of 402 undergraduates (Years 2–4) in a China–United Kingdom (Sino-UK) joint engineering programme in China. We analysed the closed-ended responses using descriptive and inferential statistics (including effect sizes) and the open-ended responses using inductive thematic analysis. Quantitative results showed that interest in the subject (76.6%) and career relevance (72.8%) were the most potent motivators. In contrast, fear of making mistakes (56%) and low confidence in public speaking (51%) were the most common barriers to participation. Other constraints included language load, deference to instructors, and prior passive learning experiences. Gender and discipline differences were negligible (Cramér’s V ≤ 0.09; Cohen’s d < 0.20). A small year-of-study effect also emerged, with later-year students marginally more confident in English-medium interactions. Qualitative analysis revealed recurring themes of evaluation anxiety, demands for technical vocabulary, inconsistent participation expectations, and reliance on private coping strategies (e.g., pre-class preparation, peer support, and after-class queries). We propose a ‘motivated-but-silent’ learner profile and blocked-pathway model where cultural, linguistic, and psychological filters prevent motivation from becoming classroom voice, refining Self-Determination Theory/Expectancy–Value Theory (SDT/EVT) and Willingness to Communicate (WTC) theories for transnational engineering contexts. These findings inform practice by recommending psychological safety measures, discipline-specific language scaffolds, and culturally responsive pedagogy to unlock student voice in English-medium Instruction/Transnational Education (EMI/TNE) settings. Full article

SCALENEo (Smart ClinicAL rEasoning iN physiothErapy) is an innovative online serious game designed to improve clinical reasoning in musculoskeletal physiotherapy education. Adapted from the “Happy Families” card game, it provides an interactive, structured approach to developing students/learners’ ability to categorize clinical information into families of hypotheses. This digital platform supports both self-directed and collaborative learning, eliminating the need for continuous instructor supervision while ensuring meaningful engagement. SCALENEo features a unique feedback and scoring system that not only assesses students/learners’ decision-making processes but also promotes cautious and reflective reasoning over random guessing. By aligning with evidence-based pedagogical strategies, such as serious games and formative assessment, SCALENEo offers educators a powerful tool to reinforce critical thinking, improve student/learner engagement, and facilitate deeper learning in clinical reasoning education. Full article

Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article

The evaluation of open-ended questions is typically performed by human instructors using predefined criteria to uphold academic standards. However, manual grading presents challenges, including high costs, rater fatigue, and potential bias, prompting interest in automated essay scoring systems. While automated essay scoring tools can assess content, coherence, and grammar, discrepancies between human and automated scoring have raised concerns about their reliability as standalone evaluators. Large language models like ChatGPT offer new possibilities, but their consistency and fairness in feedback remain underexplored. This study investigates whether ChatGPT can provide stable and fair essay scoring—specifically, whether identical student responses receive consistent evaluations across multiple AI interactions using the same criteria. The study was conducted in two marketing courses at an engineering school in Chile, involving 40 students. Results showed that ChatGPT, when unprompted or using minimal guidance, produced volatile grades and shifting criteria. Incorporating the instructor’s rubric reduced this variability but did not eliminate it. Only after providing an example-rich rubric, a standardized output format, low temperature settings, and a normalization process based on decision tables did ChatGPT-4o demonstrate consistent and fair grading. Based on these findings, we developed a scalable algorithm that automatically generates effective grading rubrics and decision tables with minimal human input. The added value of this work lies in the development of a scalable algorithm capable of automatically generating normalized rubrics and decision tables for new questions, thereby extending the accessibility and reliability of automated assessment. Full article

A persistent challenge in training future philology educators is engaging students in deep textual analysis across historical periods—especially in large classes where limited resources, feedback, and assessment tools hinder the teaching of complex linguistic and contextual features. These constraints often lead to superficial learning, decreased motivation, and inequitable outcomes, particularly when traditional methods lack interactive and scalable support. As digital technologies evolve, there is increasing interest in how Artificial Intelligence (AI) can address such instructional gaps. This study explores the potential of conversational AI chatbots to provide scalable, pedagogically grounded support in philology education. Using a mixed-methods case study, twenty-six (n = 26) undergraduate students completed structured tasks using one of three AI chatbots (ChatGPT, Gemini, or DeepSeek). Quantitative and qualitative data were collected via usability scales, AI literacy surveys, and semi-structured interviews. The results showed strong usability across all platforms, with DeepSeek rated highest in intuitiveness. Students reported confidence in using AI for efficiency and decision-making but desired greater support in evaluating multiple AI-generated outputs. The AI-enhanced environment promoted motivation, autonomy, and conceptual understanding, despite some onboarding and clarity challenges. Implications include reducing instructor workload, enhancing student-centered learning, and informing curriculum development in philology, particularly for instructional designers and educational technologists. Full article

Teaching social research methodology in university courses, whether qualitative or quantitative, presents significant challenges for both instructors and students. These challenges include the availability of empirical datasets, the illustration of data analysis techniques, the simulation of research report writing, and the facilitation of scenario-based learning. Emerging AI tools, such as ChatGPT-4, offer potential support in higher education, though their effectiveness depends on the context and their integration with traditional didactic methods. This article explores the potential of AI in teaching social research methodology, with a focus on its benefits, limits and ethical considerations. Furthermore, the paper presents a case study of AI application in teaching qualitative research techniques, specifically in the analysis of solicited documents. Generative AI shows the potential to improve the teaching of social research methodology by providing students with opportunities to engage in hands-on learning, interact with realistic datasets and refine their analytical and communication skills. The integration of AI in education should, however, be approached with a critical mindset, ensuring that AI tools serve as a means to sharpen (not replace) traditional methods of learning. Full article

Introduction: Student retention is a critical issue in higher education. Universities have responded by implementing supports like early alert systems. Objective: We investigated students’ knowledge of and experiences with an early alert system designed to enhance academic persistence. Method: We surveyed (N = 356) undergraduates at a large public university in the U.S. The survey was researcher-created and administered online. Participants self-selected into the study and provided informed consent prior to beginning the study. Data were coded by experts, who achieved excellent IRR. The analyses were frequency-based to understand diverse student knowledge, experiences, and responses with early alert systems. Results: Students commonly reported experiencing negative emotions after receiving an alert, but also reported that receiving an alert motivated them to increase their course attendance, improve their study habits, and participate more in class. Finally, students indicated that receiving an early alert facilitated supportive interactions with instructors, though student communication with academic advisors was less frequent. Student recommendations for system improvement included using positive language in alerts and providing actionable guidance. Conclusions: These results provide new insight into student views of early alert systems and suggest that these systems can positively impact students in need of support. Full article

The rapid integration of generative artificial intelligence (AI) into educational environments raises both opportunities and concerns regarding assessment design, academic integrity, and quality assurance. While new generation AI tools offer new modes of interactivity, feedback, and content generation, their use in assessment remains insufficiently pedagogically framed and regulated. In this study, we propose a new framework for generative AI-supported assessment in higher education, structured around the needs and responsibilities of three key stakeholders (branches): instructors, students, and control authorities. The framework outlines how teaching staff can design adaptive and AI-informed tasks and provide feedback, how learners can engage with these tools transparently, and how institutional bodies can ensure accountability through compliance standards, policies, and audits. This three-branch multi-level model contributes to the emerging discourse on responsible AI adoption in higher education by offering a holistic approach for integrating AI-based systems into assessment practices while safeguarding academic values and quality. Full article

Student-centered teaching approaches emphasize student responsibility and learning. Flipped learning is a student-centered approach to promoting student learning. In this paper, we describe a flipped learning strategy that involves fostering collaborative, problem-based, and project-based learning in engineering courses. In addition, we delineate the role of student assessment and teacher–student interactions in the flipped learning teaching strategy. In this study, we compare academic achievement and student perceptions of instruction in a lecture-based and a flipped classroom. The results show that the students in a flipped classroom had higher grades than those in a lecture-based classroom. In addition, students in the flipped classroom assessed the learning materials, activities, and evaluations more positively than the students in the lecture-based classroom did. Students in the flipped classroom were also found to be more likely than those in the other class to perceive that they could apply the knowledge and skills they developed in the course. They also had higher motivation to learn than the students in the lecture-based class and perceived that the instructor generated a positive climate that fostered student participation. Full article

Interactive simulations have been shown to enhance conceptual understanding through multiple dynamic representations and supporting the inquiry process by offering repeated trials with rapid feedback. However, incorporating simulation-based activities into large-enrollment gateway courses presents challenges. In an effort to address this, we effectively utilized discussion/recitation sections to implement simulation-based collaborative concept development activities to promote pre-class learning. These simulation-based activities focused on three key concept areas—phases and phase changes, colligative properties, and chemical kinetics—and were integrated into a second-term general chemistry course. The impact of these activities on students’ conceptual learning, as well as their immediate and intermediate-term retention, were examined using a two-group quasi-experimental repeated-measures post-test design. Students in the experimental group participated in simulation-based concept development activities, while those in the control group received instructor-centered lectures. Post-activity and final exam assessments were used to measure students’ retention of concepts. Analysis of covariance revealed a significant difference between the two groups on the chemical kinetics assessments, indicating the effectiveness of these activities in pre-class learning and concept development. Full article

E-learning offers sustainable opportunities for university students by improving educational accessibility and inclusion; however, research exploring learning engagement to ensure continuous learning quality from the perspective of presence remains limited, especially concerning spatial presence and its combined effects with social presence. The purpose of this study is to investigate how social presence and spatial presence interact to predict learning engagement in sustainable e-learning, as well as to determine whether perceived enjoyment mediates these relationships. A total of 442 Chinese university students participated in a self-report survey. Partial least squares structural equation modeling (PLS-SEM) was used to test the conceptual model, indicating that both social presence and spatial presence play a significant positive role in e-learning engagement, with perceived enjoyment confirming its positive role as an important partial mediator. The findings make contributions to empirical research on learning engagement by optimizing presence-related factors and positive emotions in sustainable e-learning. The implications suggest that instructors and platform providers can enhance engagement by improving social presence, spatial presence, and perceived enjoyment, as outlined in the proposed solution model “social presence and spatial presence → perceived enjoyment → learning engagement”. However, due to limitations such as self-reported data, a cross-sectional study design, and cultural context, future research could incorporate behavioral data, longitudinal studies, and explore responses from diverse cultural backgrounds. Full article

Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive feedback in mitigating these gaps by providing personalized learning support to struggling learners. A learning analytics-based evaluation was conducted on 700 undergraduate students enrolled in STEM-related courses across three different departments at Beaconhouse International College (BIC). The study employed a quasi-experimental design, where 350 students received AI-driven adaptive feedback while the control group followed traditional instructor-led feedback methods. Data were collected over 20 weeks, utilizing pre- and post-assessments, real-time engagement tracking, and survey responses. Results indicate that students receiving AI-driven adaptive feedback demonstrated a 28% improvement in conceptual mastery, compared to 14% in the control group. Additionally, student engagement increased by 35%, with a 22% reduction in cognitive overload. Analysis of interaction logs revealed that frequent engagement with AI-generated feedback led to a 40% increase in retention rates. Despite these benefits, variations in impact were observed based on prior knowledge levels and interaction consistency. The findings highlight the potential of AI-driven smart learning environments to enhance educational equity. Future research should explore long-term effects, scalability, and ethical considerations in adaptive AI-based learning systems. Full article

Picturebooks have a long history as literature for literacy learning in initial teacher education (ITE) settings. Yet, the practice of “using” picturebooks solely to teach isolated skills becomes more alarming as pre-service teachers encounter classroom picturebook instruction that features diverse racial, linguistic, or ethnic communities as “plugged” into scripted curriculum without opportunities for students to respond to the socio-cultural portrayals encountered. Guidance for ITE programs is needed to ensure that the aesthetic and sociopolitical features of picturebooks are not only considered but deeply taught to pre-service teachers. Drawing from a qualitative analysis of a fifth-grade reader engaging with a picturebook featuring a character with a similar phenotype across ten days, an inductive and iterative process of data analysis identified salient moments of collaborative discussions and the ‘slow looking’ approaches she used to interact with justice-oriented picturebooks. Our findings highlight the visual, material, and multimodal ways these texts serve as mentor resources for writing and drawing, while also acting as identity-affirming texts. To conclude, we offer essential implications for ITE settings, instructors, and their students by unpacking the significance of instruction that matters most for supporting pre-service teachers as curators of justice-oriented picturebooks. Full article

The need for scalable, immersive training systems is universal and recently has been included in fields that rely on complex, hands-on processes, such as surgery operations, assembly operations, construction processes training, etc. This paper examines the potential to support immersive training via digital tool manipulation in the domain of traditional handicrafts. The proposed methodology employs Finite Element Method simulations to compute material transformations and apply them to interactive virtual environments. The challenge is to accurately simulate human–tool interactions, which are critical to the acquisition of manual skills. Using Simulia Abaqus (v.2023HF2), crafting simulations are authored, executed, and exported as animation sequences. These are further refined in Blender (v3.6) and integrated into Unity to create reusable training components called Action Animators. Two software applications—Craft Studio (v1.0) and Apprentice Studio (v1.0)—are designed and implemented to enable instructors to create training lessons and students to practice and get evaluated in virtual environments. The methodology has wide-ranging applications beyond crafts, offering a solution for immersive training in skill-based activities. The validation and evaluation of the proposed approach suggest that it can significantly improve training effectiveness, scalability, and accessibility across various industries. Full article