Sequential pattern mining method for analysis of programming learning history based on the learning process

Nakamura, Satoshi; Nozaki, Kaname; Morimoto, Yasuhiko; Miyadera, Youzou

doi:10.1109/icetc.2014.6998902

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Simpson et al [63] investigated eEDM for classrooms using sequential pattern analysis methods to discover severe expressive communication in the environment of general education. Nakamura et al [64] proposed a sequential pattern analysis method to extract good knowledge from learning histories of programming courses. The authors developed a tool for collecting learning histories.…”

Section: Sequential Pattern Analysis Methodsmentioning

confidence: 99%

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

et al. 2021

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The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

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Section: Sequential Pattern Analysis Methodsmentioning

confidence: 99%

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

et al. 2021

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“…Quality data provide us with the transparency needed to understand what's happening in classrooms, schools, and entire education system. [13] In Moodle system logs are maintained by "log", "log_display" and log_queries. The log data are stored in the "log" table and two other tables are used to display the data through the system reports.…”

Section: La and Edm Methodsmentioning

confidence: 99%

Sequential Pattern Mining Model to Identify the Most Important or Difficult Learning Topics via Mobile Technologies

Doko

Bexheti

Hamiti

et al. 2018

Int. J. Interact. Mob. Technol.

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The paper aim is to come up with methodology for performing video learning data history of learner’s video watching logs, video segments or time series data in accordance with learning processes via mobile technologies. To reach this goal, it is introduced a theoretical method of sequential pattern mining specialized for learning histories in identifying the most important or difficult learning. Based on this method, it is designed a model for understanding and learning the most difficult topics of students topics. The user will be able to use and access the model through mobile technologies when and where he/she wants. The performed video learning history data of learner’s video watching logs consists of functions that are responsible for collection of stop/replay/backward data activities, generation of sequence from the collected learning histories, extraction of important patterns from a set of sequences, and findings of learner’s most difficult/important topic from the extracted patterns. The paper mainly describes the model for understanding and learning the most difficult topics through the sequential pattern mining method. Implementing the method to use in mobile phones is considered as future aim.

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“…As an important area in data mining, sequential rule mining has attracted a great deal of attention and many interesting methods have been proposed. Several of the frequently used methods are as follows: association rule mining [10,15], sequential pattern discovery [20,26,27,32], inter-transactional mining [8], and periodic pattern and episode mining [3,14,33]. However, few of the existing algorithms consider mining the sequential patterns with concrete time information and they either only incorporate the event sequence one after another or mine with limited time tag information.…”

Section: Challenges and Literature Reviewmentioning

confidence: 99%

Evolving temporal association rules in recommender system

Zhou

Hirasawa²

2017

Neural Comput & Applic

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This research involves implementation of genetic network programming (GNP) and ant colony optimization (ACO) to solve the sequential rule mining problem for commercial recommendations in time-related transaction databases. Excellent recommender systems should be capable of detecting the customers' preference in a proactive and efficient manner, which requires exploring customers' potential needs with an accurate and timely approach. Due to the changing nature of customers' preferences and the differences with the traditional find-allthen-prune approach, the interesting temporal association rules are extracted by the metaheuristics, genetic algorithms-based method of GNP. Additionally, a useful model is constructed using the obtained rules to forecast future customer needs and an ACO approach to evolve the online recommender system continuously. The methodology is experimentally evaluated in a real-world application by analysing the customer database of an online supermarket.

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Sequential pattern mining method for analysis of programming learning history based on the learning process

Cited by 10 publications

References 6 publications

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

Sequential Pattern Mining Model to Identify the Most Important or Difficult Learning Topics via Mobile Technologies

Evolving temporal association rules in recommender system

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