in a Mobile Environment Wongpanya Nuankaew Rajabhat Mahasarakham - - PowerPoint PPT Presentation

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An In Institution Recommender System Based on Student Context xt and Educational In Institution in a Mobile Environment

Wongpanya Nuankaew

Rajabhat Mahasarakham University, Maha Sarakham, Thailand

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Topics of f Presentation

• The Important of the Research
• Research Methodology
• Application
• Results
• Discussion
• Conclusion

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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The Im Important of The Research

• The United Nations (UN) mentioned the need for “A world with equitable

and universal access to quality education at all levels, to health care and social protection, where physical, mental and social well-being is assured [1]”

• The 2015 World Education Forum held at Incheon, South Korea, showed that

“The future vision of education is fully captured by the proposal to ensure inclusive and equitable quality for all educational institutions as well as promote lifelong learning opportunities for all learners [2]”

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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The Im Important of The Research

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Statement of f the problem

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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The Im Important of The Research

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Research Methodology

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Application

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Application

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Data Gathering

Institution Respondents

RMU: Rajabhat Mahasarakham University 478 Students MSU: Mahasarakham University 345 Students UP: University of Phayao 286 Students Total: 1,109 Students

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Results and Discussion

• The results of the model analysis and prediction
• The decision tree model
• Association rule
• Model measurement

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Decision Tree Model

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Association Rule

Rules Conditions Actions

Factor 1 Factor 2

Rule 1 The Popularity of the University = 5 Trust in Institutions = 5 It matches RMU equal to 17.02 %. While, suitable for MSU equal to 55.32 %, and appropriate to UP equal to 27.66 %. Rule 2 The Popularity of the University = 4 Skills of learners = 4 It matches RMU equal to 29.53 %. While, suitable for MSU equal to 44.49 %, and appropriate to UP equal to 25.98 %. Rule 3 The Popularity of the University = 3 Family Income = 5 It matches RMU equal to 4.76 %. While, suitable for MSU equal to 4.76 %, and appropriate to UP equal to 90.48 %. All data collected: 1,109 Samples RMU 478 samples (43.10 %), MSU 345 samples (31.11 %), and UP 286 samples (25.79 %)

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Model Measurement

Predicted Conditions Precision

RMU MSU UP

Predicted RMU 58 160 18 67.80% Predicted MSU 277 19 4 92.33% Predicted UP 143 166 264 46.07%

Recall

57.95% 46.38% 92.31%

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Discussion

• Two depth levels is reasonable for this research
• The model classification and association rules present

the distribution of data, which show a relationship between the university and students

• The results of performance is a high level of accuracy

(69.03 %).

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Conclusion

• To develop an application intended to gather the

particular characteristics and attitudes of students

• Use the gathered data for prediction and selection of

the institution

• Decision tree and Association rule methods were

utilized to develop the functions

• Data was collected from 1,109 students
• High accuracy is equal to 69.03 %

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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ACKNOWLEDGMENTS

• This paper was supported financially by

Siam Crystal Consulting Company Limited.

• The authors would like to thank the

researchers, participants, and technicians for their efforts toward the completion of this work.

An Institution Recommender System Based on Student Context and Educational Institution in a Mobile Environment

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Thank you 