Soft Skills assessment in Art and Globalization

Haro-Garcia, Noemí de; Comas-Lopez, M.; Hincz, Kajetan Piotr; Mazalu, Mirela; Sacha, G. M.

doi:10.1145/3284179.3284215

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Some studies have shown the positive impact of assessment e-rubrics. Studies reported that e-rubrics are effective tools for performing formative and summative assessments [14,18,1,15], promoting self-assessment [21], and measuring soft skills in Art subjects-both related to peer and self-evaluation [12]. E-rubrics are powerful resources for peer assessment [17].…”

Section: E-rubrics For Assessmentmentioning

confidence: 99%

Designing an Android-Based Peer Assessment E-Rubric

Cahyani

Setiawan

Hanayeen³

2022

Int. J. Interact. Mob. Technol.

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Abstract—Previous research has shown that peer assessment has beneficial effects on students’ learning. Inspired by the need of students to involve peer assessment for gaining some constructive feedback for their works, such as in translation and writing courses, we developed an android-based e-rubric. Android was favored as the assessment platform due to its customizability and affordability. Two classes of English Department with 54 students participated in this research. Interviews and focus-group discussions were conducted to obtain data for needs analysis and collect users’ feedback, whereas the online survey was done to measure the students’ satisfaction. The e-rubric was developed through eight stages: (1) needs analysis, (2) development of rubrics, (3) expert validation, (4) creating the e-rubric app, (5) implementation, (6) users’ feedback, (7) product refinement, and (8) survey of users’ satisfaction. The results show that the developed e-rubric app is useful for supporting collaborative works among students as it allows them to assess each other’s strengths and weaknesses. Moreover, the android platform gives them a handy-and-engaging experience for completing the assessment. In this paper, we review relevant literature justifying the development of the app, outline the formulation steps, and explain how the app can be used in the classroom. Taking the limitations of the app development into account, we propose some recommendations for future research.

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Section: E-rubrics For Assessmentmentioning

confidence: 99%

Designing an Android-Based Peer Assessment E-Rubric

Cahyani

Setiawan

Hanayeen³

2022

Int. J. Interact. Mob. Technol.

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“…The first one includes classification problems with all data and the second To start the classification process, we must define the three classes and understand their significance. As we have shown in Table 1, we have defined Excellent as the students with a final score of [9,10], Remarkable as the students with a final score of (5.5-9) and Fail as the students with a final score equal or below 5.5. We have increased the standard value of failure to 5.5 in order to have a small margin of error in the classification process.…”

Section: Classification Using Data From Previous Yearsmentioning

confidence: 99%

“…An example of a study about this is [9], which also uses data of this study to apply supervised machine learning algorithms. In addition, [10] shows that good predictors in soft skills can be found in bad attitudes or strategies.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment

et al. 2021

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Distance learning has been adopted as a very extended model during COVID-19-related confinement. It is also a methodology that can be applied in environments where people do not have easy access to schools. In this study, we automatically classify students as a function of their performance and we describe the best self-learning methodologies in distance learning, which will be useful both in confinement or for people with difficult access to schools. Due to the different learning scenarios provided by the different confinement conditions in the COVID-19 pandemic, we have performed the classification considering data before, during, and after COVID-19 confinement. Using a field experiment of 396 students, we have described the temporal evolution of students during all courses from 2016/2017 to 2020/2021. We have found that data obtained in the last month before the final exam of the subject include the most relevant information for a correct detection of students at risk of failure. On the other hand, students who obtain high scores are much easier to identify. Finally, we have concluded that the distance learning applied in COVID-19 confinement changed not only teaching strategies but also students’ strategies when learning autonomously.

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“…Moreover, students' marks may be predicted using clustering as [34] or applying supervised machine learning algorithms as [35,36]. In other articles, it has been pointed out that those approximations can be also good indicators to predict the future marks of a student in soft skills subjects [37].…”

Section: Introductionmentioning

confidence: 99%

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

et al. 2023

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This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students’ performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students’ marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students’ wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement.

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Soft Skills assessment in Art and Globalization

Cited by 5 publications

References 20 publications

Designing an Android-Based Peer Assessment E-Rubric

Designing an Android-Based Peer Assessment E-Rubric

Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

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