Sequential Pattern Mining System for Analysis of Programming Learning History

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

doi:10.1109/dsdis.2015.120

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, sequential pattern mining to investigate which sequences of behavior differed between high or low levels of performance in the assessments was applied [26], and in a similar study, researchers employed a sequential pattern mining algorithm, Sequential Pattern Discovery using Equivalence classes (cSPADE), on gathered log data to explore whether differences exist between learners who viewed the SRL-prompt videos and those who did not [27]. Another study developed a theoretical method of sequential pattern mining specialized for learning histories in a programming exercise [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Assisting Educational Analytics with AutoML Functionalities

2022

View full text Add to dashboard Cite

The plethora of changes that have taken place in policy formulations on higher education in recent years in Greece has led to unification, the abolition of departments or technological educational institutions (TEI) and mergers at universities. As a result, many students are required to complete their studies in departments of the abolished TEI. Dropout or a delay in graduation is a significant problem that results from newly joined students at the university, in addition to the provision of studies. There are various reasons for this, with student performance during studies being one of the major contributing factors. This study was aimed at predicting the time required for weak students to pass their courses so as to allow the university to develop strategic programs that will help them improve performance and graduate in time. This paper presents various components of educational data mining incorporating a new state-of-the-art strategy, called AutoML, which is used to find the best models and parameters and is capable of predicting the length of time required for students to pass their courses using their past course performance and academic information. A dataset of 23,687 “Computer Networking” module students was used to train and evaluate the classification of a model developed in the KNIME Analytics (open source) data science platform. The accuracy of the model was measured using well-known evaluation criteria, such as precision, recall, and F-measure. The model was applied to data related to three basic courses and correctly predicted approximately 92% of students’ performance and, specifically, students who are likely to drop out or experience a delay before graduating.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Assisting Educational Analytics with AutoML Functionalities

2022

View full text Add to dashboard Cite

show abstract

“…Each element of the pattern can be contained in the union of the keywords bought in a set of user watching logs, as long as the difference between the maximum and minimum user watching log-times is less than the size of a sliding time window. [2] The GSP (Generalized Sequential Patterns), algorithm [3] discovers new algorithm considering time constraints, sliding time windows, and taxonomies in sequential patterns. Empirical evaluation shows that GSP scales linearly with the number of data-sequences in video watching keyword topics, and has very good scale-up properties with respect to the number of watching logs per data-sequence and number of keyword topic stop/replay/backward per student.…”

Section: Sequential Pattern Mining Model Of Performing Video Learning Data Historymentioning

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.

View full text Add to dashboard Cite

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.

show abstract

“…The union of the keywords bought in a set of user watching logs includes every element of the pattern. It is like that as long as the distinction among the maximum/minimum user watching log-times is less than the size of a sliding time window (Nakamura et al, 2015). An sample can be if the teacher describes a time window of four months, a student who watched the video "KT1" on Monday, "KT2" on Saturday, and then "KT3" and "KT4" in a single day six month afterwards can still support the pattern "KT1" and "KT2", pursued by "KT3" and "KT4".…”

Section: Sequential Pattern Mining Model Of Performing Video Learningmentioning

confidence: 99%

Sequential Pattern Mining Model of Performing Video Learning History Data to Extract the Most Difficult Learning Subjects

2018

View full text Add to dashboard Cite

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.

show abstract

Sequential Pattern Mining System for Analysis of Programming Learning History

Cited by 5 publications

References 8 publications

Assisting Educational Analytics with AutoML Functionalities

Assisting Educational Analytics with AutoML Functionalities

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

Sequential Pattern Mining Model of Performing Video Learning History Data to Extract the Most Difficult Learning Subjects

Contact Info

Product

Resources

About