Chotikakamthorn, Nopporn

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Chotikakamthorn, Nopporn
Alternative Name
Chotikakamthorn, N.
Chotikakamthornb, Nopporn
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Email
nopporn.ch@kmitl.ac.th

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    Item type:Publication,
    Using Mozilla Hubs for Online Teaching: A Case Study of An Innovation Design Method Course
    (2023-01-01)
    Poolsawas, Banyapon
    ;
    Chotikakamthorn, Nopporn
    During the Covid-19 pandemic, video conferencing platforms such as Zoom Online, MS Team Meeting, and Google Meets have been primary remote teaching tools. 3D immersive and non-immersive social platforms such as Mozilla Hubs have been studied as alternative tools for organizing remote teaching. This study aimed to assess the usability of Mozilla Hubs when applied to remote teaching and compared it to that of the widely used Zoom platform. An undergraduate course on innovation design methods was selected as the case study. Students enrolled in this course were divided into two groups. Distance learning was conducted through the Zoom platform for the first group of students. In contrast, the other group participated in the course activities through the Mozilla Hub platform within a non-immersive setting. The students in the Hubs group were requested to participate in the Hub pre-training class a week before the first week of the course's lecture. Six everyday tasks requiring student interaction with each of the two platforms were selected for the study. Usability was measured in terms of efficiency and ease of use. The time taken to complete each of the selected tasks was used to measure the efficiency of each platform. The System Usability Scale (SUS) questionnaire was used to measure ease of use. For most tasks, both platforms yielded comparable results regarding task efficiency. The only exception is for the room transition task, where the results differed between the two platforms depending on whether the teleport or 3D navigation methods were chosen by students in the Hubs group in order to complete the task. Discussion on the factors affecting the efficiency of the room transition task was provided. Using the SUS questionnaire, it was found that both platforms yielded comparable SUS scores of 68.91 and 70.66 for the Zoom and Mozilla Hubs platforms, respectively. Similar ease-of-use results were due to the offering of the Hubs pre-training class to the students using the Mozilla Hubs platform.
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    Item type:Publication,
    Blockchain-based Learning Credential Revision and Revocation Method
    (2020-10-07)
    San, Aye Mi
    ;
    Chotikakamthorn, Nopporn
    ;
    Sathitwiriyawong, Chanboon
    Many blockchain-based learning credential systems have been proposed to reduce fraud and improve verification efficiency. In addition to a method for issuing and verifying credentials, a solution is needed to support the revision and revocation of an issued credential record. For the case of learning credentials, depending on how a revocation policy affects credential use that occurred before the revocation date, an additional mechanism may be needed for credential revision. Current digital learning credential methods offer only a revocation mechanism. So they do not fully meet such unique requirement in the education context. In this paper, a blockchain-based method for learning credential revision and revocation is proposed. It makes use of the revision and revocation addresses assigned to each batch of issuing credentials. To revise (revoke) one or more credentials, the proposed method stores the revision (revocation) list as a message in the OP_RETURN field of the revision (revocation) transaction, with the revision (revocation) address as one of its outputs. The concept of a local credential id has been introduced to allow a revision (revocation) list to be efficiently stored on a blockchain system. It is based entirely on a blockchain system and does not require any centralized authority. It is also applicable to most blockchain systems. A comparative study of the proposed method against existing credential revocation methods is also provided.
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    Assessing the Effectiveness of Smartphone Usage to Interact with Learning Materials in Independent Learning Outside of Classrooms among Undergraduate Students
    (2021-03-01)
    Vongjaturapat, Sununthar
    ;
    Chotikakamthornb, Nopporn
    ;
    Yimyamc, Panitnat
    Clearly, the smartphone is increasingly playing a greater role in everyday life, thus providing opportunities to evaluate how well the use of the smartphone meets the requirements of undergraduate students in independent learning outside of a classroom setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphone usage to interact with learning materials in independent learning outside of classrooms, the need for smartphone support, and the fit of devices to tasks as well as performance. First, the study used interviews, observation, and survey data to identify what are the most important constructs of smartphones that stimulate students to interact with learning materials in independent learning outside of classrooms. Based on the findings from the exploratory study and Task Technology Fit theory, we postulated the Navigation design, Ergonomic design, Content support, and Capacity as the essential dimension of the smartphone construct. Then, we proposed a research model and empirically tested hypotheses with the structural model analysis. The results reveal a significant positive impact of task and technology on TTF for smartphone usage to interact with learning materials in independent learning outside of classrooms; it also confirmed the TTF and performance have a direct effect on actual use.
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    Item type:Publication,
    On-Chain Verifiable Credential with Applications in Education
    (2024-07-01)
    Chotikakamthorn, Nopporn
    ;
    Mi San, Aye
    ;
    Sathitwiriyawong, Chanboon
    A verifiable credential (VC) has been standardized and applied in vari-ous domains, including education. Due to its immutability, blockchain has been considered and used for credential issuance and verification. Most existing methods, however, are not compatible with the W3C VC stan-dard. In this paper, an on-chain VC issuance and verification method has been described. The method is based on the standard VC data model and applicable to any credential type. It decomposes a VC document into a VC template and the corresponding value array(s). This allows a VC to be issued on-chain in the Bitcoin BTC network, which has a limited data-embedding capacity. The proposed method reduces blockchain resource consumption due to the reusability of a VC template. In addition, it allows the use of a concise VC fingerprint format instead of a full VC for credential exchange. Two issuance modes, namely the full on-chain and partial on-chain, are proposed targeting different use cases. The proposed method has been applied for issuing and verifying two learning credential types. The method was evaluated on the Bitcoin Testnet to measure time and space complexities. With the reduced-size VC fingerprint, the proposed method can embed a VC on a traditional paper-based credential as a compact-sized QR code. The proposed method ofiered faster VC issuance and verification than an existing standard-based verifiable credential method.
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    A COMPARATIVE STUDY OF FOREARM AND HAND MUSCLE USAGE DURING HANDWRITING PRACTICE USING HANDWRITING TOOL
    (2022-01-01)
    Vongjaturapat, Sununthar
    ;
    Chotikakamthorn, Nopporn
    The foundation of education is learning how to write and is considered an essential skill. The early years of education are dedicated to learning how to hold a pencil and how to form letters and words. This research aims to investigate whether there are differences in muscle activity usage between various digital handwriting tools and a standard pencil. We compare the electromyogram of five muscles: upper trapezius (TRAP), biceps brachii (BB), extensor digitorum communis (EDC), flexor digitorum superficialis (FDS), and first dorsal interosseous (FDI) during elementary school’s practice of forming letters and words. Five types of (digital) handwriting devices were used. The results showed that using Chromebooks, indicating a trend of increasing in TRAP and BB muscle activities. However, using Chromebook, indicating a trend of reducing in FDI muscle activity. Moreover, when practicing forming letters and words with iPad Pro and Livescribe pens, subjects had the lowest TRAP and BB muscle activity. When using a pencil, it indicated a trend of increasing EDC, FDS and FDI muscle activities. The findings also suggest that a Boogie Board Sync, without line space on the black screen of slate, may create difficulty in controlling their strokes on the surface properly, strokes beautification and their aesthetics, and these may ultimately affect muscle activity. Thus, this study indicated a potential lower energy consumption and lower health risks with the digital handwriting technology. Therefore, for preventing long-term negative effects on fine motor skills development digital handwriting tools may be a suitable interface solution for learning environments
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    Item type:Publication,
    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.