Table 4.

The final set of papers

Reference	Modules	Personalization features	Students’ characteristics	Recommended Item
(Kopeinik et al., 2017)	material repository, tags repository, frequent tags extractor, domain modeling, tags recommender	tagging	used tags by the student	suitable tags for the uploaded materials
(Mutahi et al., 2017)	user manager, content manager, attention manager, context manager, notification manager	performance	content interaction patterns, comments, questions, affective state	resource and activity
(Wongwatkit et al., 2017)	learning diagnostics module, learning style diagnostics module, mastery learning-based guided-inquiry learning mechanism module	learning problems, learning styles	current understanding	learning activities
(Gong et al., 2018)	knowledge component recognition, knowledge graph, exercise generation	proficiency level	time spent on exercises, score, the ability of memory	exercises with a suitable degree of difficulty
(Hongthong et al., 2018)	mobile application with four main modules, including interfacing, content repository, student assessment, and student feedback response modules	performance and preferences	score	guidance to cyber security awareness
(Klašnja-Milićević, Ivanović, et al., 2018)	learner module, domain module, application module, adaptation module, the recommendation module	interests and knowledge	needs and previously acquired knowledge	learning content
(Klašnja-Milićević, Vesin, et al., 2018)	learner-system interaction module, recommendation module [tags recommendation - recommendation of resources - reports generator], data storage module [tag repository - learner model]	educational goals, learning history	used tags	learning resource, tags
(Perišić et al., 2018)	learning object module, student module, user interface module, adaptation module, visualization module, and reporting module	learning style	general information (name and surname, date of birth, email, interest), learning progress (average grade, learning style, time spent in the course, action), information about the student’s actions (viewed, loaded, deleted, graded, submitted, posted), duration of the sessions, learning object attendance, time spent on the learning object, and number of visits of the learning object	learning material, semantic report
(Lee et al., 2018)	contents registration, management, and recommendations	learning history	video contents data, types of similar contents, sharing subjects, contents log, satisfaction, and comment data	learning video contents
(Guan et al., 2019)	personal information management, learning course plan management, course selection, assessment, achievement management, learning ability identification	learning ability	knowledge points, length of course learning, number of the learned courses, course credits, specialty	personalized curriculum
(Troussas et al., 2019)	students’ repository, students’ modeling, materials generator, recommender, hints, and trophies repository	knowledge level, learning style	age, (pre-existing knowledge on a domain, current knowledge level, knowledge level on previous concepts (scores and concepts)), preferred learning styles and techniques	individualized hints, possible collaborators, learning material, trophies
(Mimis et al., 2019)	students’ repository, students’ modeling, rank prediction, the recommendation module	performance level	score (national baccalaureate score, first-year score, score of class council of the second year), students ranking in accordance to other students, quarterly rank in each subject, (age, social motivation)	guidance to a career path
(Jagušt et al., 2019)	communication (server communication, lesson delivery, group work delivery, and progress monitoring modules), central (database, multimedia content repository, event log), adaptivity and aggregate data calculation, (lesson authoring and conducting, and lesson management (for teachers))	performance level, knowledge level	relative score to other students, time spent on tasks, activities solved	activity, visual representation of a lesson, suitable time to finish an activity
(Ch et al., 2019)	sentence reformation, summarization, factual sentence identification, trial test generation and evaluation, identification of the less confident portion	performance level	score (of the provided trial exams)	sections to revise
(Bhaskaran et al., 2019)	System interaction module, Off-line modeling, Recommendation engine	learning style and knowledge level	personal data, preferences, dominant meaning words, behavior	courses
(Fakooa et al., 2019)	student ontology, English verb ontology, admin panel, ANN module	learning style, level of knowledge	A score of quizzes, time spent on the quiz, text, and visual contents	quizzes and verb ontology
(Segal et al., 2019)	difficulty ranking module, the recommendation module	student performance	grades, number of retries, and time spent solving questions.	suitable problem sets or exams to student’s ability, topics to strengthen
(Nian et al., 2019)	recognition module of expression information, the personalized recommendation module	performance, emotions	score, expression	courses
(Troussas et al., 2019b)	students’ module, domain knowledge adaptation module, assessment adaptation module, advice provider module	knowledge level and preferences	scores	personalized guidance and questions
(Saito et al., 2020)	clustering module, prediction module, the recommendation module	submission history, ability chart	scores, current knowledge, goal	learning path recommendation
(Ma et al., 2020)	advanced automated assessment module, peer tutor recommender module	learning performance	scores	peer tutor
(Nurzaman et al., 2021)	students modeling, learning style identification, material repository, assessment, recommender	performance level, learning style	score (from teacher and systems)	learning resource
(Y. Zhang, 2021)	students’ repository, resources repository, model generator, recommender	Students’ history	Students’ evaluation score for each resource	auxiliary English teaching resources