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Asia-Pacific Forum on Science Learning and Teaching, Volume 19, Issue 1, Article 8 (Jun., 2018) |
Predicting learning outputs and retention through neural network artificial intelligence in photosynthesis, transpiration and translocation
Ananta Kumar JENA
Department of Education, Assam University Silchar-788011, Assam, INDIA
Email: akjenaaus@gmail.comReceived 12 Aug., 2017
Revised 27 Jan., 2018
Contents
- Abstract
- Introduction
- Methodology
- Analysis and Results
- H1 There are misconceptions of students in science
- H2 There are significant effects of Neural Network Artificial Intelligence Approach on the prediction of learners’ learning outputs.
- H3 There are significant effects of Neural Network Artificial Intelligence Approach on achievement and retention of science learning.
- Findings and Discussion
- Conclusion
- References
- Appendix I
Artificial Intelligence is a branch of computer science connects, classifies, differentiates, and elaborates the domains of learning in neural network, a paradigm shift is using in the construction of knowledge. In this pretest-posttest single group experimental design, neural network artificial intelligence used to investigate the existing misconception status of the participants, and predicted the learning outcomes, and retention of learning. The study aimed to assess the effects of neural network artificial intelligence approach on the achievement and retention in science learning. Forty students of a class were participated in this study, and out of them five students found having 60% to 80% of misconceptions assessed in the misconception test before exposed to the neural network artificial intelligence approach. It resulted that the mean of posttest score was statistically significant in different from the mean of the pre test score. It was resulted that input layer and first hidden layer were related with the output of the artificial intelligence.
Keywords: Artificial intelligence, neural network, photosynthesis, translocation, transpiration
