• Corpus ID: 15930690

Participation-based Student Final Performance Prediction Model through Interpretable 1 Genetic Programming : Integrating Learning Analytics

@inproceedings{2014ParticipationbasedSF,
  title={Participation-based Student Final Performance Prediction Model through Interpretable 1 Genetic Programming : Integrating Learning Analytics},
  author={},
  year={2014}
}
  • Published 2014
1 Participation-based Student Final Performance Prediction Model through Interpretable 1 Genetic Programming: Integrating Learning Analytics, Educational Data Mining and 2 Theory 3 4 5 ABSTRACT 6 Building a student performance prediction model that is both practical and understandable for users is a 7 challenging task fraught with confounding factors to collect and measure. Traditionally, most prediction 8 models are difficult for teachers without a significant background in probability to… 
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References

SHOWING 1-10 OF 74 REFERENCES
Predicting GPA and academic dismissal in LMS using educational data mining: A case mining
In this paper, we describe an educational data mining (EDM) case study based on the data collected from learning management system (LMS) of e-learning center and electronic education system of Iran
Student success system: risk analytics and data visualization using ensembles of predictive models
TLDR
A core research challenge for designing early warning systems such as S3 is discussed and an ensemble method for predictive modeling using a strategy of decomposition is proposed, which provides a flexible technique for generating and generalizing predictive models across different contexts.
Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment
TLDR
Predicting student failure by looking for changes in user's activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour is revealed.
Course correction: using analytics to predict course success
TLDR
A predictive analytic model is created for the University of Phoenix to identify students who are in danger of failing the course in which they are currently enrolled and to prioritize students for intervention and referral to additional resources.
Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming.
TLDR
A grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a positive or negative way from the perspective of Multiple Instance Learning (MIL).
Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning
TLDR
The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses.
Predicting students' final performance from participation in on-line discussion forums
TLDR
To determine how the selection of instances and attributes, the use of different classification algorithms and the date when data is gathered affect the accuracy and comprehensibility of the prediction, a new Moodle module for gathering forum indicators was developed and different executions were carried out.
Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems
TLDR
A learning based metho d that can automatically determine how likely a student is to give a correct answer to a problem in an intelligent tutoring system is proposed.
The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks
TLDR
The results show that the finer the granularity of the skill model, the better the authors can predict student performance for their online data, and it was the 39 skill model that performed the best for the standardized test data.
Using Activity Theory to Understand the Systemic Tensions Characterizing a Technology-Rich Introductory Astronomy Course
In this report of our research on a computer-based three-dimensional (3-D) modeling course for learning astronomy, we use the central tenets of activity theory to analyze participation by
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