# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 4 Results and Discussion
### Deep Learning-Based RS in E-Learning Research
The proposed approach was implemented by clustering a collection of learners and training a long short-term memory (LSTM) model to predict their learning paths and performance. A personalized full-path learning recommendation was then selected from the results of path prediction for a full practical path recommendation specifically for test learners. Ref. [88] proposed a recommendation framework for e-learning based on deep learning to solve the limitation of effective methods in e-learning. The proposed model was based on a strong capability to learn from a training set and improvements over previous methods. The model was trained using the traditional k-nearest neighbor (KNN) method, which guarantees the model's accuracy. Their system recommends new items whose similarity cannot be calculated. Additionally, their approach greatly reduces the heavy burden of a running system, which is helpful in the actual practice of RSs. The authors concluded that the framework offers a new recommendation method for more personalized learning in the future. Ref. [106] proposed a deep learning-based RS approach called neural collaborative filtering (NCF) for predicting the grade a student will earn in a course that he/she plans to take in the next term. The deep learning-inspired approach provided added flexibility in learning the latent spaces compared to previous grade prediction methods based on matrix factorization (MF), where students and courses are represented in a latent \"knowledge\" space. The proposed approach incorporated instructor information in addition to student and course information. Finally, for proper analysis of the learned model parameters,the authors assumed that the embeddings obtained for students, courses, and instructors should be non-negative. This non-negative NCF model, referred to as the NCF_nn_ model, added a rectified linear unit (ReLU) on the embedding layer of NCF. Ref. [115] proposed a novel deep reinforcement learning framework (DRE) for adaptively recommending exercises to students with the optimization of three objectives: review and explore, smoothness of difficulty level, and engagement. The framework proposed two different exercise Q-networks (EQN) to generate recommendations for an agent. The first one is a straightforward yet effective EQNM with the Markov property, where the next recommendations to students only depend on their current exercising performance. In contrast, the second is a more sophisticated EQNR with a recurrent manner, where they tracked all students' exercising history to make decisions. Both architectures are model-free; thus, the authors did not need to calculate the state transitions in the exercise space. The authors also proposed three novel reward functions to formally capture and quantify those three objectives so that the DRE could update and optimize its recommendation strategy by interactively receiving students' performance feedback. Ref. [116] introduced a novel goal-based RS based on adaptations of RNN for suggesting courses to help students prepare for target courses of interest, personalized to their estimated prior knowledge background and zone of proximal development. The authors validated several model variations against test sets representing the tasks of grade prediction, prerequisite prediction, and course selection prediction. These three prediction validations were chosen to suggest whether the approach warranted testing in the wild. The proposed approach model's definition was based on a popular variant of RNNs called LSTM. LSTM was used because it helps RNNs to learn temporal dependencies by adding several gates, which can retrain and forget select information. Ref. [122] proposed a time slice imputation for personalized goal-based RSs in higher education.