Transfer-Learning Methods in Programming Course Outcome Prediction

The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in educational contexts, differences between courses set huge challenges for the generalizability of these methods. For example, traditional machine-learning methods assume identical distribution in all data—in our terms, traditional machine-learning methods assume that all teaching contexts are alike. In practice, data collected from different courses can be very different as a variety of factors may change, including grading, materials, teaching approach, and the students. Transfer-learning methodologies have been created to address this challenge. They relax the strict assumption of identical distribution for training and test data. Some similarity between the contexts is still needed for efficient learning. In this work, we review the concept of transfer learning especially for the purpose of predicting the outcome of an introductory programming course and contrast the results with those from traditional machine-learning methods. The methods are evaluated using data collected in situ from two separate introductory programming courses. We empirically show that transfer-learning methods are able to improve the predictions, especially in cases with limited amount of training data, for example, when making early predictions for a new context. The difference in predictive power is, however, rather subtle, and traditional machine-learning models can be sufficiently accurate assuming the contexts are closely related and the features describing the student activity are carefully chosen to be insensitive to the fine differences.Peer reviewe

Course outcome prediction with transfer learning methods

In computer science, introductory programming course is one of the very first courses taken. It sets the base for more advanced courses as programming ability is usually assumed there. Finding the students that are likely to fail the course allows early intervention and more focused help for them. This can potentially lower the risk of dropping out in later studies, because of the lack of fundamental skills. One measure for programming ability is the outcome of a course and the prediction of these outcomes is the focus also in this thesis. In educational context, differences between courses set huge challenges for traditional machine learning methods as they assume identical distribution in all data. Data collected from different courses can have very different distributions as there are many factors that can change even between consecutive courses such as grading, contents, and platform. To address this challenge transfer learning methods can be used to as they make no such assumption about the distribution. In this thesis, one specific transfer learning algorithm, TrAdaBoost, is evaluated against selection of traditional machine learning algorithms. Methods are evaluated using real-life data from two different introductory programming courses, where contents, participants and grading differ. Main focus is to see how these methods perform in the first weeks of the course that are educationally the most critical moments

A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

Designing a programming-based approach for modelling scientific phenomena

We describe an iteratively designed sequence of activities involving the modelling of 1- dimensional collisions between moving objects based on programming in ToonTalk. Students aged 13-14 in two settings (London and Cyprus) investigated a number of collision situations, classified into six classes based on the relative velocities and masses of the colliding objects. We describe iterations of the system in which students engaged in a repeating cycle of activity for each collision class: prediction of object behaviour from given collision conditions, observation of a relevant video clip, building a model to represent the phenomena, testing, validating and refining their model, and publishing it – together with comments – on our web-based collaboration system, WebReports. Students were encouraged to consider the limitations of their current model, with the aim that they would eventually appreciate the benefit of constructing a general model that would work for all collision classes, rather than a different model for each class. We describe how our intention to engage students with the underlying concepts of conservation, closed systems and system states was instantiated in the activity design, and how the modelling activities afforded an alternative representational framework to traditional algebraic description

A programme management approach for ensuring coherence in IS (higher) education

This paper examines IS higher education, concentrating on issues of ‘coherence’ in IS curricula. While curriculum coherence can be jeopardized by poor curriculum design, misalignment between module content and/or misalignment between module or course aims can cause serious coherence issues over time. Misalignment of this type is exacerbated by the traditional processes of curriculum (re)design, which rely heavily on the (singular) interpretation of highly abstract documents, such as module syllabi and course specifications – often produced by curriculum designers in isolation. To improve curriculum coherence, this paper examines the use of a programme management framework as a means of (a) ‘humanizing’ the abstract aims and goals of curricula schemes and (b) managing the delivery and evolution of curricula in relation to the stakeholders in the process of delivery. The practical use of the framework is examined in the context of a Masters-level course in ‘Information Systems Management’. An action research approach is used to demonstrate the practical utility of the framework in terms of (a) improving communication of curricula, (b) improving the coherence between modules and between modules and course and (c) removing content redundancy. Guidelines are presented that generalize the findings in order that key practices may be adopted by others