## 3 Proposed Work  
Based on the analysis of the existing research on elective course recommendation, this work comes up with a recommendation strategy which predicts the domain expertise of the student, and then recommends elective courses of similar domain expertise. The computer science and engineering (CSE) curriculum courses in Vellore Institute of Technology (VIT) are considered as the data set for the elective course recommendation. In this curriculum, there are 39 core courses and these courses are mapped with nine major domains of CSE identified by subject matter experts (SME) as specified in Table 1. Domain ID \(d_{9}\) is not considered for any kind of analysis in this work since it has the collection of non-mathematical and non-CSE courses. Every elective course offered for the said programme falls under any of these eight domains. Initially, the core course content's keywords are trained using the GloVe learning algorithm [33] with deep learning models, along with the predefined targeted domain. GloVe is a pre-trained word embedding vector toolkit with a new bilinear regression model which is a combination of global matrix factorization and local context window methods. This model outperforms the existing models on word analogy, word similarity and named entity recognition tasks. Text classification is an important task in natural language processing (NLP) and it can be performed using the deep learning models since it can be categorized into multiple predefined categories. Texts are mapped into a mathematical representation before feeding it into the neural network, and are represented as word embeddings. Words of similar meaning can be learned and represented as word embeddings, in which each word of the higher dimensional text is mapped into a lower dimension real number vector space [34]. This is the entire idea behind the text classification progresses around the appropriate use of word embeddings. The semantically similar words tend to have similar embedding \begin{table}
\begin{tabular}{c c} \hline \hline
**Domain ID** & **Domain Name** \\ \hline \(d_{1}\) & Programming \\ \(d_{2}\) & Algorithms \\ \(d_{3}\) & System Structure \\ \(d_{4}\) & Discrete Mathematics \\ \(d_{5}\) & Differential Equations \\ \(d_{6}\) & Networks and Communications \\ \(d_{7}\) & Software Engineering \\ \(d_{8}\) & Data Visualization \\ \(d_{9}\) & Non-CSE/Math Courses \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of domains identified by SMEs. vectors. Here, the global vectors for word representation (GloVe) learning [33] is used to learn embeddings for massive text mined across the internet. Word2 vec [14] is an algorithm that transforms words into vectors using GloVe, so that words with similar meanings end up lying close to each other. Moreover, it allows us to use vector arithmetic to work with analogies [35; 36]. The spaCy [16; 17] python package from Stanford University is an implementation of GloVe from weblogs. Using the embedding vectors, sequences are generated and ordered by timesteps. Each time step corresponds to a word in the sequence. Every neural network cell processes the input at that time step (t), plus the conditional probability of words occurring before the current timestep (1 to t - 1). The generated neural network encoding is mapped onto a softmax activation layer in a dense fully connected neural network, whose cross-entropy loss function is solved by Adam's gradient descent. The proposed elective course recommendation adapted the NLP tools, such as spaCy, GloVe for text processing, and categorizes the text using the deep learning models. A language can be processed using a deep learning algorithm that works similar to human brains. Neural networks have a network of neurons activated by an activation function and extract features out of these networks by propagating useful information from one layer to another layer. Multi-class text classification tasks are well tackled by neural networks, and the deep learning model used in this approach is the gated recurrent unit (GRU).