Author(s): Piyawat Tratsaranawatin, Tanawat Jariyapoom
Background: Recently, most students register for classes without any information and options to support their decision because the systems used do not recommend the registration instructions of each class and the number of students is increasing. Moreover, most teachers have a lot of and limited tasks, and most office staff are responsible for paper documents which can be easily lost and the right time of making an appointment between teachers and students is also limited.
Aims: The purposes of this research were 1) to design the recommendation system of student registration with collaborative filtering, 2) to develop the recommendation system of student registration with collaborative filtering, and 3) to assess the competency of recommendation system of student registration with collaborative filtering.
Findings: The results of the research revealed that the research instrument used was the model of assessment system. The sample group of subjects was the students of King Mongkut’s University of Technology North Bangkok with simple random sampling. The data was analyzed by simple statistics: percentage and standard deviation (S.D.).
Conclusion: This research could be concluded that the success found that 1) the participants of the recommendation system of student registration with collaborative filtering were divided into three groups: students, teachers and office staff, 2) the recommendation system of student registration with collaborative filtering consisted of four development phases: planning, analysis, design, implementation, and 3) the overall efficiency of the recommendation system of student registration with collaborative filtering was at a high level (xËÂ?? = 4.51 ) and the standard deviation (S.D.) was of 0.61. This showed that the performance of the recommendation system was efficient.
Practical Implications: The students had the guideline for course enrolment through the smart registration system and the system could enhance the student’s learning outcomes. The teachers could reduce the procedures and time of consultation or finding some more information to enhance the students’ learning efficiency.
Originality/Value: This research studied the recommendation system of student registration with the data of the students’ learning outcomes by implementing the theory of data recommendation and the collaborative filtering to recommend and predict the students’ learning outcomes.