Construction of Online Teaching Learning Effect Prediction Model Based on Artificial Neural Network Algorithm

At present, many studies on online learning are mostly based on the amount of energy and time learners invest in certain learning behaviors, and there is relatively little research on predicting learning outcomes. Therefore, this article introduces artificial neural network algorithms to quantify the concept of online teaching evaluation indicators as deterministic data as input and online teaching effectiveness as output. Empirical research is conducted on artificial neural network (ANN) training, and an online teaching effectiveness prediction model is constructed. Finally, the established learning effect prediction model is used to assess the test data set, and an ideal prediction result is obtained. The research results show that the F1 value of the proposed algorithm can reach 96.13%, and the MAE (Mean absolute error) of the algorithm can reach 1.37. This shows that this method has certain advantages. The establishment of the online teaching learning effect prediction model provides an effective method and means for evaluating students' online learning behavior scientifically and reasonably, and also provides reference suggestions for teaching design and courseware development.