A MODEL FOR DEVELOPING STUDENTS' SOFT SKILLS USING LEARNING ANALYTICS
Keywords:
Soft Skills, artificial intelligence, Learning Analytics, test answers, assessment, personal development, AI model.Abstract
This article proposes a new model for assessing and developing students' Soft Skills using Learning Analytics and Artificial Intelligence (AI) technologies. While traditional assessment systems focus on academic knowledge, the current labor market also considers Soft Skills, in particular, skills such as communication, teamwork, critical thinking, and leadership, to be important. The article discusses the advantages of assessing these factors through AI, the mechanisms for determining the individual approach to the student using Learning Analytics, as well as the trajectory of personal development. The research is conducted on the basis of synthetic test and question-answer data (synthetic dataset). Each student's response is analyzed by an AI model and classified as "Good", " Average ", or "Poor". The results are saved in CSV and Excel formats and visualized through diagrams. This methodology simplifies digital monitoring in educational institutions and allows for accurate identification of each student's Soft Skills level, as well as providing them with appropriate guidance. The article concludes with information about the AI architecture, platform description, and assessment system components required for the project.
References
Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist , 57(10), 1380–1400.
Romero, C., Ventura, S. (2020). Educational Data Mining and Learning Analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 10(3).
Dede, C. (2010). Comparing Frameworks for 21st Century Skills. Harvard University Report .
Goleman, D. (1998). Working with Emotional Intelligence . Bantam Books. [4]
OECD. (2019). Skills for 2030 – Future of Education and Skills. OECD Education 2030 Project
Chatti, MA, Dyckhoff, AL, Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning , 4(5-6), 318–331.
OpenAI. (2023). Transformers Library Documentation . https://huggingface.co/docs/transformers.
McNamara, D. S., & Graesser, A. (2012). Reading comprehension and strategy instruction. Handbook of Psychology .
Boud, D., & Falchikov, N. (2006). Aligning assessment with long-term learning. Assessment & Evaluation in Higher Education , 31(4), 399–413.
Panadero, E., & Broadbent, J. (2018). Developing evaluative judgment: a self-regulated learning perspective. Assessment & Evaluation in Higher Education , 43(4), 579–592.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 .
Selwyn, N. (2015). Data entry: Towards the critical study of digital data and education. Learning, Media and Technology , 40(1), 64–82.
