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Course recommendation systems can support students success. An important component of such a system is the prediction of the grade students can expect when they take a course. In this paper, different algorithms for predicting grades are used and compared.Linear regression models providethe better results; furthermore, they have the advantage of being comprehensible, which enables users to better assess the limitations of the model and thus decide how much trust they want to place in the system.
With cross-disciplinary academic interests increasing and academic advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher. We build on the findings and methodologies of a quickly developing literature around prediction and recommendation in higher education and develop a novel recurrent neural network-based recommendation system for suggesting courses to help students prepare for target courses of interest, personalized to their estimated prior knowledge background and zone of proximal development. We validate the model using tests of grade prediction and the ability to recover prerequisite relationships articulated by the university. In the third validation, we run the fully personalized recommendation for students the semester before taking a historically difficult course and observe differential overlap with our would-be suggestions. While not proof of causal effectiveness, these three evaluation perspectives on the performance of the goal-based model build confidence and bring us one step closer to deployment of this personalized course preparation affordance in the wild.
Neben klassischen Beratungs- und Unterstützungsangeboten haben viele Hochschulen in den letzten Jahren innovative Konzepte entwickelt, um Studierende von Beginn an gezielt zu fördern und damit den Studienerfolg zu steigern. Viele dieser Angebote sind passgenau auf bestimmte Zielgruppen (MINT-Studierende, Bildungsausländer etc.) zugeschnitten.Hochschulen greifen zunehmend auch auf digitale Angebote zurück, um die fachliche Passung der Studierenden sicherzustellen (Online Assessment) sowie
die Vermittlung von Studieninhalten zu intensivieren (Blended-Learning-Kurse).
Die Identifizierung von vier Best Practice Modellen zeigt, dass die Angebote von Hochschulen besonders erfolgversprechend sind, deren Maßnahmen über den gesamten Studienverlauf miteinander verknüpft und von einem Monitoring der Studierenden
hinsichtlich ihrer fachlichen Leistungen begleitet werden.
Reducing dropout rates in higher education would allow increasing the number of graduates. If one can predict early enough whether a student might drop out, targeted counseling could be put in place. This work replicates the approach of Berens et al. (2019) to predict whether students might dropout using academic performance data from their first semester. Further, the approach is extended by comparing the results of the cross-program model on specific programs of study with the results of the models trained for each specific program. The findings support the generalization of the approach of Berens et al. (2019) to the German context, which could serve to establish best practices for dropout prediction in higher education .
Course selection can be a daunting task, especially for first-year students. Sub-optimal selection can lead to bad performance of students and increase the dropout rate. Given the availability of historic data about student performances, it is possible to aid students in the selection of appropriate courses. Here, we propose a method to compose a personal-ized curriculum for a given student. We develop a modular approach that combines a context-aware grade prediction with statistical information on the useful temporal ordering of courses. This allows for meaningful course recommendations , both for fresh and senior students. We demonstrate the approach using the data of the computer science Bachelor students at Saarland University.
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Sparse Neural Attentive Knowledge-based Models for Grade Prediction
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Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation
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Grade prediction with course and student specific models
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