Developing early warning systems to predict students’ online learning performance

Hu, Ya-Han; Lo, Chi-Fang; Shih, Sheng-Pao

doi:10.1016/j.chb.2014.04.002

Cited by 237 publications

(167 citation statements)

References 23 publications

Supporting

Mentioning

152

Contrasting

Unclassified

Order By: Relevance

“…The possibility of using time as a feature to include in the prediction model must be considered. The use of time can help the detection of trends, the influence of accumulated factors and an actual study of progression, as indicated by Hu et al (2014). If predictions can be made in different temporary moments the comparison with previous predictions will help to detect trends that cannot be detected with a single prediction in a given moment.…”

Section: Tools For Results Interpretationmentioning

confidence: 99%

“…Macfadyen and Dawson (2010) and Pedro et al (2013) consider that the use of a priori models is an important limitation. White box techniques are very suitable for simple cases, particularly when there are linear relationships between variables, but they are difficult to be extrapolated to other experiences, as pointed out by Hu et al (2014) and Xing et al (2015). However, interpretation of whitebox methods is usually quite straightforward, since the underlying models are known.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Hu et al (2014) develop a system for early prediction of final student performance. Learning portfolios are used as input to the algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving the expressiveness of black-box models for predicting student performance

Villagr-Arnedo

Gallego-Durn

Largo

et al. 2017

Computers in Human Behavior

View full text Add to dashboard Cite

Early prediction systems of student performance can be very useful to guide student learning. For a prediction model to be really useful as an effective aid for learning, it must provide tools to adequately interpret progress, to detect trends and behaviour patterns and to identify the causes of learning problems. White-box and black-box techniques have been described in literature to implement prediction models. White-box techniques require a priori models to explore, which make them easy to interpret but difficult to be generalized and unable to detect unexpected relationships between data. Black-box techniques are easier to generalize and suitable to discover unsuspected relationships but they are cryptic and difficult to be interpreted for most teachers. In this paper a black-box technique is proposed to take advantage of the power and versatility of these methods, while making some decisions about the input data and design of the classifier that provide a rich output data set. A set of graphical tools is also proposed to exploit the output information and provide a meaningful guide to teachers and students. From our experience, a set of tips about how to design a prediction system and the representation of the output information is also provided.

show abstract

Section: Tools For Results Interpretationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the expressiveness of black-box models for predicting student performance

Villagr-Arnedo

Gallego-Durn

Largo

et al. 2017

Computers in Human Behavior

View full text Add to dashboard Cite

show abstract

“…For example, Hu et al collected data from 300 students and established a student risk prediction model. Experimental results revealed a 95% accuracy in predicting students' passing or failure rates based on 1-4 weeks of data [6]. Meier et al designed a neighborhood selection process to predict students' grades.…”

Section: Introductionmentioning

confidence: 99%

Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

Yang

Huang

et al. 2018

Journal of Information Processing

View full text Add to dashboard Cite

Abstract:With the rise of big data analytics, learning analytics has become a major trend for improving the quality of education. Learning analytics is a methodology for helping students to succeed in the classroom; the principle is to predict student's academic performance at an early stage and thus provide them with timely assistance. Accordingly, this study used multiple linear regression (MLR), a popular method of predicting students' academic performance, to establish a prediction model. Moreover, we combined MLR with principal component analysis (PCA) to improve the predictive accuracy of the model. Traditional MLR has certain drawbacks; specifically, the coefficient of determination (R 2 ) and mean square error (MSE) measures and the quantile-quantile plot (Q-Q plot) technique cannot evaluate the predictive performance and accuracy of MLR. Therefore, we propose predictive MSE (pMSE) and predictive mean absolute percentage correction (pMAPC) measures for determining the predictive performance and accuracy of the regression model, respectively. Analysis results revealed that the proposed model for predicting students' academic performance could obtain optimal pMSE and pMAPC values by using six components obtained from PCA.

show abstract

“…Macfadyen and Dawson (2010) used data mining method to analyze the learners' archives, predict and build the learning effectiveness model. Data mining method can also apply to explore the data to describe the status of learners (Hu et al, 2014). Association rules is one of the most famous data mining methods, which explored the relationships among the data attributes (Agrawal et al, 1993;Han et al, 2001;Chen & Weng, 2009;Weng, 2011;Sowan et al, 2013;Shabana & Samuel, 2015;Palacios et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

Chen

Chang

2017

EURASIA J MATH SCI T

View full text Add to dashboard Cite

The phenomenon of low fertility has been negative impacted on the social structure of the educational environment in Taiwan. To increase the learning effectiveness of students became the most important issue for the Universities in Taiwan. Due to the subjective judgment of evaluators and the attributes of influenced factors are always fuzzy, it is not easy to measure the learning effectiveness by using the crisp values for the qualitative factors. In additional, the negative behaviors or the infrequent learning data (rare item sets) cannot easily excavate from educational database. Therefore, a systematic mining method is proposed here by combining fuzzy set theory with data mining technology to explore the key factors of learning effectiveness from the infrequent data. A case study implemented in this paper and showed that the proposed method can effectively find the important factors and valuable patterns from the survey data. The results of this paper can provide the important information for teachers to help students increase the learning effectiveness. Finally, the conclusions are addressed at the end of this paper.

show abstract

Developing early warning systems to predict students’ online learning performance

Cited by 237 publications

References 23 publications

Improving the expressiveness of black-box models for predicting student performance

Improving the expressiveness of black-box models for predicting student performance

Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

A Study on the Rare Factors Exploration of Learning Effectiveness by Using Fuzzy Data Mining

Contact Info

Product

Resources

About