## 2 Summary of the Contributions
### Teaching and Learning  
Student grade prediction is an important educational problem for designing personalized strategies of teaching and learning. To solve this problem, in [7], a graph regularized robust matrix factorization is proposed optimized by a majorization minimization algorithm. This method integrates two side graphs built on the side data of students and courses into the objective of robust low-rank matrix factorization. As a result, the learned features of students and courses can grasp more priors from educational situations to achieve higher grade prediction results. This facilitates personalized teaching and learning in higher education.  
For developing adaptive e-learning systems, it is very helpful to provide information on how students recognize, process and store information. To improve students' learning evaluation, in [8], a method based on deep multi-target prediction algorithm using Felder-Silverman learning styles model is proposed. It uses feature selection, learning styles models, and multiple target classification to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.  
Students' performance prediction in higher education was considered in [9]. To exploit the knowledge retrieved from one problem for improving the predictive performance of a learning model for a different but related problem, the authors use transfer learning. The experimental results demonstrate that the prognosis of students at risk of failure can be achieved with satisfactory accuracy in most cases, provided that datasets of students who have attended other related courses are available.  
Paper [10] was conducted with the aim of identifying the interrelationships among topics based on the understanding of various bodies of knowledge. The study provides a foundation for topic compositions to construct an academic body of knowledge of AI.  
To this end, ML-based sentence similarity measurement models used in machine translation, chatbots, and document summarization were applied to the body of knowledge of AI. Consequently, several similar topics related to agent designing in AI were identified. The results of this study can be applied in the edutech field.  
Predicting the academic standing of a student at the graduation time can be very useful for institutions to select among candidates or in helping potentially weak students in overcoming educational challenges. In [11], this problem is solved using several ML algorithms based on different student data including individual course grades and grade point averages. This approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction. For predicting the grades of undergraduate students in the final exams, in [12], multi-view learning is applied to exploit the knowledge retrieved from data, represented by multiple feature subsets known as views. A semi-supervised regression algorithm is proposed which exploits three independent and naturally formed feature views, derived from different sources. The experimental results demonstrate that the early prognosis of students at risk of failure can be accurately achieved and could highly benefit the educational domain.