An Evaluation Model of Tecaching Assistant using Artificial Neural Network
DOI:
https://doi.org/10.21015/vtcs.v11i2.438Abstract
Nowadays, most of the academic institutes facing a low-quality problem in the educational field. One of these factors is an educational student achievement and staff teaching quality. This study presents an efficient framework for assessment and prediction of teachers’ performance in academic institutes using Artificial Neural Network (ANN) algorithm. The proposed framework designed to predict the performance quality level of teachers in order to improve the learning outcomes. The prediction model was tested effectively using the TA UCI dataset. The data consists of academic experiences for teachers as well as their experiences and grades of students in courses they taught among others. The SPSS tool was used to build the suggested prediction system. The TA data was dividing into three groups (70, 80, and 90) for training data, and (30, 20, and 10) for testing data respectively to study the dataset discrimination. The results are showing that the neural network obtained better accuracy results with (90%) in the training and (10%) in testing.Downloads
Published
2016-12-21
How to Cite
Osman, A. H. (2016). An Evaluation Model of Tecaching Assistant using Artificial Neural Network. VAWKUM Transactions on Computer Sciences, 4(1), 87–91. https://doi.org/10.21015/vtcs.v11i2.438
Issue
Section
Articles
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY