Predicting learning outputs and retention through neural network artificial intelligence in photosynthesis, transpiration and translocation http://www.ied.edu.hk/apfslt/v17

The study claimed that 60% of students had misconceptions in photosynthesis concepts because of their doubt in the previous classes. It was found that input layer and first hidden layer were strongly related with the outcomes and predicted outcome. Because the outcome dependents on the input layer and first hidden layer. It was found that the posttest score was significantly different from pre test score and delay test score. This was due to the treatment effect. Most of the researcher in the world of education found that artificial intelligence is a modern approach and this could be use in teaching learning process, mostly in engineering, statistics, and arithmetic and computer science. Out of different artificial intelligence, neural network artificial intelligence used to know its effectiveness to predict the learners’ outcome as well as achievement and retention in science learning. Objective 1 of the study based on the misconception test. It was found that 60% of students had misconception in science concept and the result was corroborated with (Cavus, 2010; Kalogirou and Mellit, 2008). However, Conrad (1987) argued that it was difficult to assess students’ misconception. Objective 2 of the study was deals with the neural network artificial intelligence and prediction of learners’ learning outcomes. In this study, it was found that input layer and first hidden layer were related with the output of the artificial intelligence. Therefore, input layer and first hidden layers were the predictors of output layer. This result was supported by (Bratko, 1993; Hendry, 1987; Monostory, 2013 and Prieto et al, 2013). In addition, objective 3 of the study reveals with the impact of Neural Network Artificial Intelligence on learners’ achievement and retention. It was found that before treatment (pre test), after treatment (posttest) and the delay test was significantly different. The mean of posttest score was significantly higher than the mean of pretest and delay test score. This was due to the treatment effect. This result was supported by (Barto and Sutton, 1997; Dautenhahn, 1995; Feldman and Yakimovsky, 1974 and Kolodziejezyk etal, 2010). From the above discussion, it was found that neural network artificial intelligence intervention has significant effect on the concepts of input layer with the concept of hidden layer to get the output layer. This is a kind of machine learning provides output after getting the inputs while hidden layers are the processors uses in computer science, logarithm, and science learning.