Use of formative assessment to improve the online teaching materials content quality

Zarqtouni, Maryem; Haddi, Adil; Allali, Hakim

doi:10.1145/3419604.3419808

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Formative assessment helps in students' learning process and enhances students learning outcomes [38,39]. Various critical teachers' prerequisites-knowledge and skills, social factors, and psychological factors-for formative assessment are found useful for improving the quality of teaching and learning [40,41]. Reference [13] develops a model of formative assessment enactment using the design-based approach of science teachers.…”

Section: Formative Assessmentmentioning

confidence: 99%

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Hooda

Rana

Dahiya

et al. 2022

Mathematical Problems in Engineering

114

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The core focus of this review is to show how immediate and valid feedback, qualitative assessment influence enhances students learning in a higher education environment. With the rising trend of online education especially in this COVID-19 pandemic, the role of assessment and feedback also changes. Earlier the assessment part is not considered the main focus in learning and teaching in HEIs, but now with the increase in online education, it is observed that the paradigm is shifted toward assessing those activities of students that enhance their learning outcomes. A lot of research work has been done on developing assessment strategies and techniques that can support learning and teaching effectively. Yet, there is limited research that looks at how methods applied in learning analytics can be used and possibly constitutes the assessment process. The objective of this work is to provide an exploratory and comparative study of how assessment and feedback practices can enhance students learning outcomes using AI. The key contribution of this study attempts to capture an outline of the most used artificial intelligence and machine learning algorithms for student success. The results showed that I-FCN performed better than other techniques (ANN, XG Boost, SVM, Random Forest, and Decision Trees) in all measured performance metrics. Also, the result of the comparative analysis study will help the educators, instructors, and administrators on how they could take the advantage of a data-driven approach, design less pressurized, more valid, reliable, constructive assessment findings, and connect the power of assessment and feedback to enhance the learning outcomes.

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Section: Formative Assessmentmentioning

confidence: 99%

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Hooda

Rana

Dahiya

et al. 2022

Mathematical Problems in Engineering

114

View full text Add to dashboard Cite

show abstract

“…Dilihat dari segi kontennya, Content Quality dalam E-Learning tergantung pada perancangan dan pengelolaan dari lingkungan pembelajaran berjalan dengan baik. Mahasiswa lebih mudah menerima konten berkualitas yang diatur dengan baik, disajikan dengan efektif, interaktif, ditulis dengan jelas, panjangnya tepat, bermanfaat, fleksibel, dan penyampaian yang tepat dan sesuai [24]. [25] menunjukkan bahwa pemberian konten media daring dalam pembelajaran ekonomi berpengaruh terhadap motivasi belajar.…”

Section: B Metode Penelitianunclassified

Peran Pembelajaran Daring, Komunikasi, Dan System E-Learning Terhadap Motivasi Belajar Mahasiswa

Fambudianto¹,

Kusuma²

2022

ijcs

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Online learning is the main learning system amid government policies that have closed face-to-face learning activities as a result of the emergence of Covid-19. Based on pre-study data, it shows that the obstacles that interfere with the learning process are learning content that is less attractive, internet networks that are less supportive, and inadequate digital devices. In the communication aspect, problems that arise, such as students who cannot ask questions directly to lecturers, and online learning which is not as interactive as face-to-face learning. Meanwhile, from the aspect of the E-Learning System, the problems that arise are the content, content, or appearance that is less attractive and interactive, information that is not optimal, not up-to-date, and E-Learning is less stable. This research was conducted to analyze the role of online learning, communication, and E-Learning systems on learning motivation. The approach is carried out using quantitative methods and processed using Structural Equation Modeling Partial Least Squares (SEM-PLS). As a result, it was found that the facilities and knowledge of the teacher had a significant positive effect on students' learning motivation with p-values of 0.00.

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“…Developed countries generally regard pre-school education as an important starting point and foundation for elite training, therefore they have laid great emphasis on it, and the teaching quality of preschool education and the faculty level of developed countries are improving constantly [1][2][3][4][5][6][7]. In China, preschool education is receiving more attention than ever before, and the government has successively issued a few important documents [8][9][10][11][12] such as the Professional Standards for Kindergarten Teachers, the Opinions of the State Council on the Current Development of Preschool Education, and the Guidelines for Kindergarten Education, etc.…”

Section: Introductionmentioning

confidence: 99%

A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning

Wang

Liu

2021

Int. J. Emerg. Technol. Learn.

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Developed countries regard preschool education as an important starting point and foundation for elite training. In recent years, preschool education has also attracted a growing attention in developing countries like China. Considering the significance of the teaching quality of preschool teachers to lifelong academic achievement, this paper designs a teaching quality evaluation model for preschool teachers based on deep learning. Firstly, a progressive system with a hierarchical structure was developed for the relevant evaluation indices. Then, the fuzzy comprehensive evaluation of each index layer and evaluation criterion was determined by the principle of fuzzy relationship synthesis. Finally, an evaluation prediction model was established based on extreme gradient boosting (XGBoost) algorithm and technology services’ ResNet (TS-ResNet), and proved effective and accurate through experiments. The research results provide a reference for the application of the proposed model in other evaluation prediction scenarios.

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Use of formative assessment to improve the online teaching materials content quality

Cited by 3 publications

References 4 publications

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Peran Pembelajaran Daring, Komunikasi, Dan System E-Learning Terhadap Motivasi Belajar Mahasiswa

A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning

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