scholarly journals THE LEARNING AN ALYTICS APPLICATION LEMO – RATIONALSAND FIRST RESULTS

2014 ◽  
pp. 226-234
Author(s):  
Margarita Elkina ◽  
Albrecht Fortenbacher ◽  
Agathe Merceron
Keyword(s):  
Path Analysis ◽  
Open Source ◽  
System Architecture ◽  
Learning Analytics ◽  
Pattern Mining ◽  
Design Principles ◽  
Sequential Pattern ◽  
Current Version ◽  
Efficient Tool ◽  
First Results

LeMo is an open source application for learning analytics, which collects data about learners' activities from different platforms. This article describes design principles of LeMo in the context of creating an efficient tool for learning analytics. Focus is on the LeMo system architecture, user path analysis employing algorithms of sequential pattern mining, and visualization of learners' activities implemented in the current version. A case stud y shows first results.

scholarly journals A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1525
Author(s):  
Felipe Vieira ◽  
Cristian Cechinel ◽  
Vinicius Ramos ◽  
Fabián Riquelme ◽  
Rene Noel ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Collection ◽  
Pattern Mining ◽  
New Technologies ◽  
Personal Information ◽  
Sequential Pattern Mining ◽  
Complex Problem ◽  
Sequential Pattern ◽  
Background Information ◽  
Professional Relationships

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.

Top-k Self-Adaptive Contrast Sequential Pattern Mining

2021 ◽  
pp. 1-15
Author(s):  
Youxi Wu ◽  
Yuehua Wang ◽  
Yan Li ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Self Adaptive

Sequential Pattern Mining Algorithm Based on Interestingness

2018 ◽  
Author(s):  
Tao Li ◽  
Shuaichi Zhang ◽  
Hui Chen ◽  
Yongjun Ren ◽  
Xiang Li ◽  
...  
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mining Algorithm

Sequential pattern mining for ICT risk assessment and management

2019 ◽  
Vol 102 ◽  
pp. 1-16 ◽  
Author(s):  
Michele D'Andreagiovanni ◽  
Fabrizio Baiardi ◽  
Jacopo Lipilini ◽  
Salvatore Ruggieri ◽  
Federico Tonelli
Keyword(s):  
Risk Assessment ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Risk Assessment And Management
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