Artificial intelligence has evolved from a dichotomy of symbolist AI and connectionist AI to a multi-paradigm AI, many of which are interdisciplinary.
However, data-driven AI is currently the predominant trend, particularly machine learning.
What applications of Machine learning can be identified in Higher Education?
Machine Learning (ML) is transforming higher education by enhancing learning, teaching, and administration.
By leveraging ML, higher education institutions can improve student engagement, optimize operations, and enhance overall learning experiences.
Dear Rey Segundo Guerrero-Proenza ,
By analyzing historical data, machine learning models can help in identifying the trends and patterns that may influence a student's decision to continue their studies. In this case, factors like academic performance, attendance, or extracurricular involvement can be considered
The benefits of edtech for higher education like machine learning are phenomenal.
https://www.ixrlabs.com/blog/applications-of-machine-learning-in-higher-education/#:~:text=By%20analyzing%20historical%20data%2C%20machine,extracurricular%20involvement%20can%20be%20considered.
Regards,
Shafagat
Machine learning has a range of uses in higher education, providing innovations in both teaching and administration. Some of these uses include the:
1. Personalized Learning - AI-based platforms can tailor the course to individual students' needs to increase engagement and understanding.
2. Automated Grading - Grading assignments and tests is time-consuming, but machine learning can automate assessments and reduce instructor burden while keeping evaluations consistent.
3. Predictive Analytics - Many institutions are using ML to study outcomes and prediction at-risk students based on the aforementioned factors.
4. Adaptive Tutoring Systems - Intelligent tutoring systems can give students real-time feedback and relevant suggestions.
5. Plagiarism and Cheating Detection - Rather than using a simple matching algorithm to determine if a students work is original content, machine learning can identify text patterns and others similarities to realign the process.
6. Chatbots and Virtual Assistants - AI supported tools, such as chatbots, will give instant responses to students for administrative purposes.
8. Curriculum Optimization - Machine learning can improve courses for their effectiveness of skill or knowledge acquisition from students and suggest changes based on students in a subject area overall.
9. Smart Classrooms
10. Automated Course Recommendation
etc
You can use Distributed Decision Tree based algorithm for classifications.
ML models analyze historical data, including grades, attendance, engagement with learning management systems (LMS) and socio-economic factors, to predict academic performance. These predictions help educators identify students at risk of poor performance or dropout, enabling timely interventions.
Benefits
1. Early Detection - Identifies students needing help before they fail or drop-out.
2. Personalized Learning - Adapts learning materials based on students strengths and weaknesses.
3. Improved Retention Rates - Helps universities reduce dropout rates by providing necessary support.
4. Efficient Resource Allocation - Enables universities to focus support efforts on students who need it most.
Rey Segundo Guerrero-Proenza
Como profesora de temas de Psicología Empresarial, puedo comentar varias aplicaciones del Machine Learning (ML) en la educación. Lo primero sería que la entrada de estos sistemas a la vida del profesional que se está formando tanto en pregrado como en postgrado, implica asumir que se está transformando la forma en que enseñamos y aprendemos, querramos o no, estemos al tanto o no, lo practiquemos o no; por tanto, nuestra preparación como docentes, requiere actualizar nuestras prácticas en función de los cambios en el escenario educativo mundial.
Una de las aplicaciones que destacan es la personalización del aprendizaje con el empleo de algoritmos de ML para adaptar el contenido educativo a las necesidades particulares de cada aprendiz, ajustando el ritmo y el nivel de dificultad según su progreso. Además, sugieren materiales adicionales o cursos relacionados que podrían ser de interés para los estudiantes, basándose en su historial de aprendizaje y preferencias.
Otro ejemplo es el análisis predictivo del rendimiento académico, mediante el cual los modelos de ML pueden analizar datos de rendimiento pasado y patrones de comportamiento para predecir estudiantes en riesgo de fracaso académico, posibilitando la planificación y ejecución de intervenciones tempranas. Esto no solo ayuda a mejorar los resultados académicos, sino que también permite a los educadores ajustar sus estrategias de enseñanza para optimizar el aprendizaje de manera intencional y particular.
No obstante, siempre me gusta resaltar los desafíos éticos que su empleo requiere, porque es lo que aporta el componente humano y la reflexión consciente de su uso como complemento y no como sustituto de los procesos complejos de pensamiento y aprendizaje. Destacando nuestro rol activo y sujetos capaces de autodeterminar nuestra conducta y alejarnos de ser meros consumidores de las posibilidades ilimitadas de las tecnologías en nuestro quehacer profesional.
Maví Pérez García BTW, talking about predictions on students failing their evaluations, several years ago I was the opponent to a PhD Thesis in Educational Technology, written by Angolan student, where he proposed one such a model. I remember he even included ethnic origin as one of the factors to be considered (he supported it by means of datasets obtained in the context were study was applied). There was a very hot discussion around that issue...
A thought-provoking question - and one that opens an important interdisciplinary dialogue.
As a psychologist and educator working in higher education for over two decades, I see that machine learning becomes truly meaningful only when it is rooted in human understanding, psychological safety, and the inner architecture of learning itself.
I'm particularly interested in how ML can support: ▸ Early recognition of academic burnout and cognitive overload ▸ Personalized learning trajectories based on emotional-cognitive dynamics ▸ Reflective analytics that allow students and educators to track inner development, not just academic scores
If we want education to be truly transformative - not only scalable - we need to ensure that AI helps us build coherence, not just efficiency.
My work focuses on integrating digital care, pedagogical identity, and mental health into holistic educational ecosystems - especially in contexts of global disruption and uncertainty.
It’s not only about adopting machine learning - it’s about aligning it with what makes us human.
Dr. Olha A. Cherepiekhina International Researcher in Mental Health, Educational Resilience & Strategic Pedagogical Design
There are several applications of ML in higher education. The MathE platform is a free e-learning platform that uses ML and AI in its recommendation system to support student to learn Math in Higher education. The platform is available at "mathe.ipb.pt" and in the section reserach publication you can find a set of research involving ML and the platform data.
I will also left you some examples here:
Chapter Application of Pattern Recognition Techniques for MathE Ques...
Chapter Evaluating Student Behaviour on the MathE Platform - Cluster...
Chapter Analyzing the MathE Platform Through Clustering Algorithms
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