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    Item Response Time Analysis Using Ex-Gaussian Distribution for Disengagement Detection in Online Low-Stakes Tests
    (2026-01-01)
    Chotikakamthorn, N.
    This study addresses the problem of detecting disengagement in online low-stakes tests used in blended learning within higher education. The detection method was developed based on an analysis of item responses and associated response times. The method applied the ex-Gaussian mixture model to response times, rather than the conventional lognormal model. The mixture component with the smallest Gaussian mean was chosen to represent the response times distribution of early correct responses. The selected mixture component was used to obtain the model’s mode, which then served as the threshold for classifying item responses into early and subsequent response groups. Based on the two classified groups, descriptive statistics and graphical visualizations were introduced to support manual inspection and provide insight into item- and person-level characteristics. A test statistic for disengagement detection was formulated based on the distribution of the number of early responses. Drawing on prior knowledge of the success probabilities associated with disengaged responses, two detection boundaries were defined to classify item-preknowledge and rapid-guessing behaviors. Unlike existing model-based methods for rapid guessing and item preknowledge behavior detections, the proposed non-parametric method does not require prior knowledge of item or person parameters, nor does it involve modeling or estimating such characteristics. The method’s performance was assessed using both real and simulated data, and results for true positive rates and false positive rates were reported under various test conditions. The findings indicate that the method’s performance improves with an increasing number of test items and a higher proportion of disengaged responses. Simulation results further demonstrated the method’s robustness to measurement error and small variations in response times, in contrast to the person-level adaptation of the NT10 and CUMP methods, whose performance varied significantly under the same conditions.
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    Navigating online learning challenges and educational infrastructure in times of crisis: Insights and solutions among Thai engineering students utilizing a mixed-methods analysis
    (2024-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Leerojanaprapa, Kanogkan
    ;
    Khwanpruk, Kankanit
    The rapid shift to online learning during COVID-19 posed challenges for students. This investigation explored these hurdles and suggested effective solutions using mixed methods. By combining a literature review, interviews, surveys, and the analytic hierarchy process (AHP), the study identified five key challenges: lack of practical experience, disruptions in learning environments, condensed assessments, technology and financial constraints, and health and mental well-being concerns. Notably, it found differences in priorities among students across academic years. Freshmen struggled with the absence of hands-on courses, sophomores with workload demands, and upperclassmen with mental health challenges. The research also discussed preferred strategies for resolution, emphasizing independent learning methods, managing distractions, and adjusting assessments. By providing tailored insights, this study aimed to enhance online learning. Governments and universities should support practical work, prioritize student well-being, improve digital infrastructure, adapt assessments, foster innovation, and ensure resilience.
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    Development of an Online Active Learning Model Using the Theory of Multiple Intelligence to Encourage Thai Undergraduate Student Analytical Thinking Skills
    (2022-09-30)
    Rodrangsee, Bovornwich
    ;
    Tuntiwongwanich, Somkiat
    ;
    Pimdee, Paitoon
    ;
    Moto, Sangutai
    This research aim was to create an online active learning (OAL) model using Multiple Intelligence Theory (MIT) to promote Thai undergraduate analytical thinking skills (ATS). Eight experts assisted with their expertise in the model’s development and five for the model’s final assessment. The research tools used consisted of an interview form and a model assessment form. The results showed that the PPICE learning model consisted of nine elements and five steps. The five steps were preparation and problem determination (Step 1), problem analysis (Step 2), study and collect relevant information to practice thinking (Step 3), conclusion and presentations (Step 4), lesson summary and evaluation (Step 5). The expert's assessment of the PPICE Model revealed that overall the model was at its 'best' level (mean = 4.63, SD = .048). Moreover, the experts judged the model's nine components as the 'best' (mean = 4.74, SD = 0.38), with measurement and evaluation second (mean = 4.70, SD = 0.45), and teaching and learning activity processes third (mean = 4.66, SD = 0.48).