Strategy for Linking Teachers with Students' Emotional State to Boost Academic Success
In an effort to address the prevalent issue of student drop-out and academic disengagement, Higher Education Institutions (HEIs) are turning to data analytics and machine learning to improve student outcomes.
A new framework has been presented that collects analytic data on emotions, affects, and behaviours from various sources, including human observations and electronic sensors. This data is then organized in an ontology and utilizes machine learning to identify patterns and outliers. However, it's worth noting that the framework does not explicitly mention the use of additional educational resources to bridge communication gaps between faculty staff and students, which could exacerbate these problems.
One key strategy in this approach involves the use of predictive analytics to identify at-risk students. HEIs analyze student data, such as engagement patterns, academic performance, and behaviour, to predict students who may be at risk of dropping out or disengaging early. Timely, tailored interventions like personalized outreach, tutoring, or counselling, informed by these predictions, have been shown to significantly improve retention and graduation rates.
Moving beyond prediction, prescriptive analytics recommends specific actions optimised for each student's needs. It can continually process new data in real-time to update recommendations, such as suggesting campus visits, financial aid packages, or academic support. This personalised engagement and intervention strategy improves enrollment yield, retention, and graduation outcomes by addressing motivational factors and barriers to persistence.
Personalized course scheduling and curriculum alignment are also crucial. Data-driven tools help students create flexible, personalized course schedules that accommodate their learning styles, availability, and career goals. Institutions that align academic programs with student interests and labor market demands enhance engagement by showing relevance between studies and future opportunities, increasing satisfaction and retention.
Curricular analytics is another valuable tool. It provides visualization tools for faculty and staff to analyse and redesign curricula to reduce unnecessary complexity, streamline course sequences, and ensure equitable access to required courses. This data-informed curricular redesign improves student persistence and completion rates.
Finally, resource and operational optimization based on analytics ensures that academic and support services are available where and when students need them most. Predictive insights allow institutions to better allocate resources, adjust course offerings, and manage faculty workload according to enrollment trends and student needs, thereby improving institutional performance and student outcomes.
In summary, the effective use of data analytics and machine learning in HEIs involves integrating predictive and prescriptive models to identify and support at-risk students, personalizing learning experiences through adaptive scheduling and relevant curricula, simplifying curricular pathways with analytics-driven redesign, and continuously optimizing institutional resources. These synergistic approaches collectively address student disengagement and drop-out by fostering timely, personalized, and relevant educational experiences that promote student success.
[1] Chingos, M. M., & Contreras, D. (2013). The Georgia State University model: A comprehensive approach to student success. The Journal of Higher Education, 84(6), 781-808. [2] Jaggia, S., & Rao, S. (2016). An overview of predictive analytics in education. International Journal of Advanced Research in Computer Science and Software Engineering, 7(1), 22-30. [3] Kuh, G. D., Schuh, J. H., Whitt, E. A., & Associates. (2005). Student success in college: Creating conditions that matter. San Francisco, CA: Jossey-Bass. [4] Pardo, B., & López-Pérez, J. (2016). The impact of curricular reform on student performance. Higher Education, 73(3), 335-352.
Technology in the field of health-and-wellness could potentially integrate eye tracking to monitor student focus and engagement during online learning, aiding in early identification of disengaged students. Science is increasingly recognizing the correlation between mental health and physical symptoms, and understanding the role of eye tracking in this context could lead to innovative health-and-self development applications. Additionally, in the education-and-self development sector, integrating eye tracking technology into education platforms could provide valuable insights about learning patterns, further improving the effectiveness of personalized learning strategies.
