Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data

Tan, Tai; Bezbradica, Marija; Crane, Martin

doi:10.1016/j.future.2021.08.026

Cited by 45 publications

(30 citation statements)

References 35 publications

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“…We found that the connections between cryptocurrencies decrease greatly without noise and trend effects: large cryptocurrencies such as Bitcoin, Ethereum and Ripple do not see to affect the cryptocurrency market as they did before the cleaning process, since there is no strong connection between them and other cryptocurrencies. This result is in line with [ 70 ], where the Eigenvalue Clipping method was also used to clean the education-related correlation matrix.…”

Section: Research Methodologysupporting

confidence: 87%

“…On the other hand, the trend effect found in other correlated systems [ 70 ] might be found in the cryptocurrency market. Briefly speaking, a trend among cryptocurrencies means that they tend to move together in terms of price values.…”

Section: Research Methodologymentioning

confidence: 93%

“…Furthermore, this cleaning method is straightforward to implement, with the guaranteed efficiency as it keeps the information part, i.e., after the cleaning process, the trace of the correlation matrix remains unchanged [ 80 ]. This method has shown good performance in different studies and has been applied widely to different topics such as programming education, portfolio optimization and signal processing [ 70 , 81 , 82 ]. The outstanding performance of the Eigenvector clipping encourages us to choose this method for our cleaning scheme.…”

Section: Research Methodologymentioning

confidence: 99%

See 2 more Smart Citations

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

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We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

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Section: Research Methodologysupporting

confidence: 87%

Section: Research Methodologymentioning

confidence: 93%

Section: Research Methodologymentioning

confidence: 99%

See 1 more Smart Citation

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

Self Cite

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“…In slightly different approach, the authors of Mai et al [14], Zeineddine et al [15] predicted the learning outcomes using two different data mining techniques. However, the obtained accuracy, 79% and 75.9%, are moderate.…”

Section: Related Workmentioning

confidence: 99%

“…This stage covers all of the raw data processes to build the final dataset. Inclusion and exclusion of data selection tasks included, adding new attribute possibility or amending an existing one, as well as data cleaning [14].…”

Section: Data Preparationmentioning

confidence: 99%

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

Alnawas

Al-Jawad

Alharbi

2022

jeasd

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E-Learning has become an essential teaching approach during the COVID-19 pandemic. All over the world, various internet-based learning management systems (Google classroom, Moodle, etc.) were adopted to convey knowledge and enhance learning outcomes. However, measuring learning outcomes and knowledge acquisition in E-Learning environment is a controversial issue. To this end, this paper aims to predict learning outcomes using data mining techniques. Student data are collected and analyzed to construct the prediction model. The collected data covered students from various undergraduate studies. Cross-Industry Standard Process for Data Mining is used as a research model. The obtained result shows the significant of some attributes in predicting learning outcomes. Four correlation-based attributes selection schemas are applied. The selected attributes are examined using four data mining algorithms: random forest, k-nearest neighbors, Decision Tree, and neural network. The overall performance of the constructed mining models is evaluated using various performance measures: Accuracy, Precision, Recall and F1-score are calculated. Overall, an 86% accuracy is secured.

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Learning Programming Difficulties: Toward an Integrated Conceptual Framework

Alrusaini

2023

Lecture Notes on Data Engineering and Communications Technologies

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Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data

Cited by 45 publications

References 35 publications

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

Learning Programming Difficulties: Toward an Integrated Conceptual Framework

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