Planning the Design and Execution of Student Performance Assessment in Serious Games

Serious Games (SGs) are used to support knowledge acquisition and skill development. For this, there is a need to measure the results achieved (both during and after students play) to ensure the game effectiveness. In this context, the aim is to develop and evaluate the AvaliaJS, a conceptual model to structure, guide and support the planning of the design and execution of the student's performance assessment in SGs. AvaliaJS has two artifacts: a canvas model, for high-level planning, and an assessment project document, for more detailed specifications of the canvas. To analyze and exemplify the use of the model, the artifacts were applied to three ready-made games as a proof of concept. In addition, the quality of AvaliaJS was evaluated by experts in SGs development and assessment using a questionnaire. The results of experts' answers confirm a good internal consistency (Cronbach's alpha α = 0.87) which indicates that AvaliaJS is correct, authentic, consistent, clear, unambiguous and flexible. However, the model will need to be validated during the process of creating a new game to ensure its usability and efficiency. In general, AvaliaJS can be used to support the team in the planning, documentation and development of artifacts and data collection in SGs, as well as in the execution of the assessment, learning measurement and constant and personalized feedback for students.

DEFINITIONS AND MEASURES OF WORKPLACE LEARNING AND JOB SATISFACTION IN THE CONTEXT OF INDUSTRY 4.0

People obtain more knowledge, information and skills on the job market. Thus, the management of human resources is being more crucial for every organization. The concept of workplace learning is becoming an essential factor for businesses success, especially in the times of crisis. It helps companies to stay competitive on the market and helps them to respond promptly to the challenges caused by rapid changes due to the fourth industry revolution. The concept of “Industry 4.0” represents the fast gains of developed technologies and applications in industries that help with increasing the productivity, but also require the employees to continuously improve and learn in order to be able to integrate with new technological improvements. However, different authors have different definitions of what job satisfaction really is. It is an extensively researched yet a barely understood phenomenon in organizations. Job satisfaction yields to subjective perception of how one feels about work. Thus, various factors, both external and internal, are associated with it. This paper aims to provide the definitions and reviews of active workplace learning measures used in empirical studies in human resource development and most commonly used measures for job satisfaction. The research paper can provide organizations and practitioners with the information on different approaches that can be used to measure and identify employees’ preferences and to help them find a way to satisfy their workers and keep them at their side in this fast-changing work environment. Keywords: workplace learning measurement, job satisfaction measurement, industry 4.0

A Conceptual Framework for Investigating and Mitigating Machine Learning Measurement Bias (MLMB) in Psychological Assessment

Given significant concerns about fairness and bias in the use of artificial intelligence (AI) and machine learning (ML) for assessing psychological constructs, we provide a conceptual framework for investigating and mitigating machine learning measurement bias (MLMB) from a psychometric perspective. MLMB is defined as differential functioning of the trained ML model between subgroups. MLMB can empirically manifest when a trained ML model produces different predicted score levels for individuals belonging to different subgroups (e.g., race, gender) despite them having the same ground truth level for the underlying construct of interest (e.g., personality), and/or when the model yields differential predictive accuracies across the subgroups. Because the development of ML models involves both data and algorithms, both biased data and algorithm training bias are potential sources of MLMB. Data bias can occur in the form of nonequivalence between subgroups in the ground truth, platform-based construct, behavioral expression, and/or feature computing. Algorithm training bias can occur when algorithms are developed with nonequivalence in the relation between extracted features and ground truth (i.e., algorithm features are differentially used, weighted, or transformed between subgroups). We explain how these potential sources of bias may manifest during ML model development and share initial ideas on how to mitigate them, recognizing that the development of new statistical and algorithmic procedures will need to follow. We also discuss how this framework brings clarity to MLMB but does not reduce the complexity of the issue.

EKSPLORASI KESIAPAN DOSEN DAN MAHASISWA MENJALANI PEMBELAJARAN JARAK JAUH DI MASA PANDEMI COVID-19

The COVID-19 pandemic has changed the learning process from face-to-face to online learning. Therefore, universities are forced to prepare their learning management system’s infrastructure quickly. Students and lecturers’ readiness for e-learning are also crucial. This study aims to investigate lecturers and students’ readiness for online learning during this pandemic. Furthermore, lecturers and students’perception about the constraints and advantages of online learning were also explored in this study. Respondents in this study were 1036 students and 354 lecturers from various universities in Indonesia. Students’ readiness for online learning was measured by the Online Learning Readiness Survey/OLRS and the results showed that students in this study were ready because they had a high score of self-efficacy in technology and high learning motivation during the pandemic. Meanwhile, lecturers' readiness was measured by Teacher Readiness for Online Learning Measurement/TROLM and we found that lecturers were ready because they had a high score in communicating via computer and in self-directed learning to continue utilizing the technology. These results contribute to research related in online learning during the pandemic and provide important implications for University’s management in dealing with changes in education.AbstrakPandemic COVID-19 telah mengubah proses belajar dari tatap muka menjadi belajar secara daring (pembelajaran jarak jauh/PJJ). Oleh karena itu perguruan tinggi diminta untuk mempersiapkan infrastruktur pembelajaran jarak jauh secara cepat. Kesiapan mahasiswa dan dosen dalam menjalankan PJJ pun menjadi sangat penting. Penelitian ini bertujuan untuk menggali kesiapan dosen dan mahasiswa menjalani PJJ selama masa pandemi. Selanjutnya, peneliti juga memetakan persepsi dosen dan mahasiswa tentang kendala dan keuntungan dalam pembelajaran secara daring. Responden penelitian ini adalah 1036 mahasiswa dan 354 dosen dari berbagai perguruan tinggi di Indonesia. Kesiapan mahasiswa mengikuti PJJ diukur dengan menggunakan Online Learning Readiness Survey/OLRS. Hasilnya adalah bahwa mahasiswa/i dalam studi ini siap menjalankan PJJ. Alasannya adalah dikarenakan mereka memiliki efikasi diri yang tinggi menggunakan teknologi dan motivasi belajar yang tetap baik di masa pandemi. Sementara itu kesiapan dosen mengajar diukur dengan menggunakan Teacher Readiness for Online Learning Measurement/TROLM. Ditemukan bahwa dosen-dosen siap menjalankan PJJ. Alasannya dikarenakan mereka memiliki efikasi diri yang tinggi berkomunikasi melalui komputer dan memiliki self-directed learning yang juga tinggi untuk terus belajar menggunakan teknologi. Di samping berkontribusi terhadap penelitian terkait PJJ di masa pandemi, hasil penelitian ini juga memberikan implikasi penting bagi pengelola perguruan tinggi dalam menghadapi perubahan pendidikan di masa yang akan datang.

Development of contactless measurement device for overhead contact line

In order to reduce maintenance labor of overhead contact lines (OCL), a contactless measurement device for OCL was developed. This device is mounted on a roof of a vehicle of a train and measures static three-dimensional positions of wires of OCL and detects positions of OCL fittings without touching the OCL while the train is running. We proposed hybrid sensing method that combines stereo measurement by image processing with structure measurement by laser range scanners and it realized to measure OCL geometry with high-precision even in sections with complicated OCL structure. In addition, we developed position detection method of the OCL fittings that can cope with changes in height and stagger of OCL by using machine learning. Measurement data of OCL contactless measurement device is static position of OCL without influence of a probe such as a pantograph. With this device, the OCL static position can be measured continuously instead of at each support point or dropper point. For maintenance of OCL, the criterion of OCL is defined as a static position. The device is utilizable for OCL maintenance and it sophisticate maintenance of OCL. For example, this device realize the height difference measurement of the crossing section, which has been conventionally measured by maintenance workers. In addition, the device are utilizable for OCL fittings inspection. Maintenance workers can check the image of OCL fittings without on foot into the field. Furthermore the static position data of OCL can be used to create simulation model of OCL dynamic behavior. Using this model, it is possible to know the dynamic response of cases where various pantographs pass at various speeds. Running tests was conducted on commercial line, and the performance of the device was verified when running at a speed of 130 km/h. The results shown that the repeated measurement accuracy is within 10 mm, and the OCL fitting detection rate is 90 % or more.

Comparative Analysis of Mental Expenses for End-Level Students in Dealing with Online and Direct Learning with the NASA-TLX Method

Limitation of direct physical interaction related to the Covid-19 pandemic has an impact on the education sector, where all learning activities are carried out online to limit physical interactions. Online learning methods are considered more flexible to do when compared to direct learning methods. This research was conducted to determine how much difference the mental load felt by final year students in online learning and direct learning. Measurement of a mental load was carried out using the National Aeronautics and Space Administration Task Load Index (NASA-TLX) method by distributing questionnaires containing six subscales of mental load measurement to final year students of four different study programs, including Management, Accounting, Informatics Engineering, and English. The six subscales used include Mental Demands, Physical Demands, Temporal Demands, Own Performance, Effort, and Frustration. Based on the results of the average calculation of the four sample groups, it shows that the mental load of online learning is 0.4% greater than direct learning, 81.3% and 80.9%, respectively.