Role of personality in computer based learning

With the rapid growth of technology enhanced learning, new mediums for learning have emerged. One of these mediums is computer based learning where the main concern is how to design a computer based learning system which takes into consideration the learners' differences. Personality is considered as one of the most critical sources of individual differences. This study investigates how personality differences within learners can affect computer based learning, through a comprehensive review of the literature. The highlighted results from the obtained nineteen studies are: (a) the most referred to personality model in computer based learning is MBTI; (b) personality traits affect how learners prefer learning content and learning approach like collecting information, communicating with instructor and peer, study behavior, acting and performing; (c) a new model of personality variables should be considered in computer based learning by taking all interested researchers and practitioners into accounts; and (d) the traditional questionnaire approach which is still the pre-dominant method to identify the learner's personality; and this needs to be changed with new potential of big data and learning analytics. Furthermore, this study presents a new implicit approach using learning analytics instead of questionnaire-based approach to identify the learner's personality.     

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

The popularity of intelligent tutoring systems (ITSs) is increasing rapidly. In order to make learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner or the context. One of the possible adaptation techniques is adaptive item sequencing by matching the difficulty of the items to the learner's knowledge level. This is already accomplished to a certain extent in adaptive testing environments, where the test is tailored to the person's ability level by means of the item response theory (IRT). Even though IRT has been a prevalent computerized adaptive test (CAT) approach for decades and applying IRT in item‐based ITSs could lead to similar advantages as in CAT (e.g. higher motivation and more efficient learning), research on the application of IRT in such learning environments is highly restricted or absent. The purpose of this paper was to explore the feasibility of applying IRT in adaptive item‐based ITSs. Therefore, we discussed the two main challenges associated with IRT application in such learning environments: the challenge of the data set and the challenge of the algorithm. We concluded that applying IRT seems to be a viable solution for adaptive item selection in item‐based ITSs provided that some modifications are implemented. Further research should shed more light on the adequacy of the proposed solutions.     

An application of adaptive games-based learning based on learning style to teach SQL

The fact that each student has a different way of learning and processing information has long been recognised by educationalists. In the classroom, the benefits derived from delivering learning content in ways that match the student's learning style have also been identified. As new modes of delivery of learning content such as computer-assisted learning systems (e.g. eLearning) have become increasingly popular, research into these has also identified the benefits of tailoring learning content to learning styles. However, in games-based learning (GBL), the adaptation based on learning style to enhance the educational experience has not been well researched. For the purpose of this research, a game with three game modes has been developed: 1) non-adaptivity mode; 2) a mode that customises the game according to the student's learning style identified by using a learning style questionnaire; and 3) a mode that has an in-game adaptive system that dynamically and continuously adapts its content according to the student's interactions in the game.This paper discusses the term adaptivity in a GBL context and presents the results of an experimental study investigating the differences in learning effectiveness of the different game modes compared to a paper-based learning. The study was performed with 120 Higher Education students learning the database language SQL (Structured Query Language). The results show that the game developed, regardless of mode, produced better learning outcomes than those who learned from a textbook while adaptive GBL was better in terms of allowing learners to complete the tasks faster than the other two game versions.     

Experience beyond knowledge: Pragmatic e-learning systems design with learning experience

With the growing demand in e-learning system, traditional e-learning systems have dramatically evolved to provide more adaptive ways of learning, in terms of learning objectives, courses, individual learning processes, and so on. This paper reports on differences in learning experience from the learner’s perspectives when using an adaptive e-learning system, where the learner’s knowledge or skill level is used to configure the learning path. Central to this study is the evaluation of a dynamic content sequencing system (DCSS), with empirical outcomes being interpreted using Csikszentmihalyi’s flow theory (i.e., Flow, Boredom, and Anxiety). A total of 80 participants carried out a one-way between-subject study controlled by the type of e-learning system (i.e., the DCSS vs. the non-DCSS). The results indicated that the lower or medium achievers gained certain benefits from the DCSS, whilst the high achievers in learning performance might suffer from boredom when using the DCSS. These contrasting findings can be suggested as a pragmatic design guideline for developing more engaging computer-based learning systems for unsupervised learning situations.     

Learning presence as a moderator in the community of inquiry model

This study of over 2000 US college students examines the Community of Inquiry framework (CoI) in its capacity to describe and explain differences in learning outcomes in hybrid and fully online learning environments. We hypothesize that the CoI model's theoretical constructs of presence reflect educational effectiveness in a variety of environments, and that online learner self-regulation, a construct that we label “learning presence” moderates relationships of the other components within the CoI model. Consistent with previous research (e.g., Means, Toyama, Murphy, Bakia, & Jones, 2009; Shea & Bidjerano, 2011) we found evidence that students in online and blended courses rank the modalities differently with regard to quality of teaching, social, and cognitive presence. Differences in help seeking behavior, an important component of self-regulated learning, were found as well. In addition, results suggest teaching presence and social presence have a differential effect on cognitive presence, depending upon learner's online self-regulatory cognitions and behaviors, i.e. their learning presence. These results also suggest a compensation effect in which greater self-regulation is required to attain cognitive presence in the absence of sufficient teaching and social presence. Recommendations for future research and practice are included.     

Adaptation provisioning with respect to learning styles in a Web‐based educational system: an experimental study

Personalized instruction is seen as a desideratum of today's e‐learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style‐based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates some of the limitations of similar systems. The adaptive methods used as well as their implementation in a dedicated system (WELSA) are presented, together with a thorough evaluation of the approach. The results of the experimental study involving 64 undergraduate students show that accommodating learning styles in WELSA has a beneficial effect on the learning process.     

Developing a survey for assessing preferences in constructivist context‐aware ubiquitous learning environments

This study developed a survey to explore students' preferences in constructivist context‐aware ubiquitous learning environments. A constructivist context‐aware ubiquitous learning (u‐learning) environment survey (CULES) was developed, consisting of eight scales, including ease of use, continuity, relevance, adaptive content, multiple sources, timely guidance, student negotiation, and inquiry learning. The survey responses were gathered from 215 university students from five universities in Taiwan. The students all had actual experience of using u‐learning systems in u‐learning environments. Both exploratory and confirmatory factor analyses showed that the CULES had high reliability and validity. The structural model revealed that the provision of realistic and close‐to‐real‐life information could enhance students' preferences for timely guidance, student negotiation, and inquiry‐learning activities. In addition, the attainment of inquiry learning is quite challenging when designing u‐learning activities, as it involves the enhancement of the other CULES scales.     

个性化学习路径推荐综述

近年来，伴随着现代信息技术的迅猛发展，以人工智能为代表的新兴技术在教育领域得到了广泛应用，引发了学习理念和方式的深刻变革.在这种大背景下，在线学习超越了时空的限制，为学习者“随时随地”学习提供了更多的可能性，从而得到了蓬勃发展.然而，在线学习中师生时间、空间分离的特征，导致教师无法及时掌握学生的学习状态，一定程度上制约了在线学习中教学质量的提升.面对多元化的学习需求及海量学习资源，如何迅速完成学习目标、降低学习成本、合理分配学习资源等问题成为限制个人和时代发展的重大问题.然而，传统的“一刀切”的教育模式已经不能满足人们获取知识的需求了，需要一个更高效、更科学的个性化教育模式，以帮助学习者以最小的学习成本最大限度地完成学习目标.基于以上背景，如何自动高效识别学习者特征，高效地组织和分配学习资源，为每一位学习者规划个性化路径，成为面向个体的精准化教育资源匹配机制研究中亟待解决的问题.系统地综述并分析了当前个性化学习路径推荐的研究现状，并从多学科领域的角度分析了对于同一问题的不同研究思路，同时也归纳总结了当前研究中最为主流的核心推荐算法.最后，强调当前研究存在的主要不足之处.     

Learning style analysis in adaptive GBL application to teach SQL

The benefits derived from delivering learning content in ways that match the student's learning style have been identified in classroom learning and eLearning. Although there is limited empirical evidence in adaptive Games-Based Learning (GBL), adaptivity has been identified to have the potential to improve learning effectiveness. This paper presents the results of a study to investigate the use of learning styles in GBL particularly in identifying the learning style in GBL including the learning style's fluctuation during the learning process. For the purposes of this study, a game with two game modes was developed: 1) non-adaptivity mode and 2) a mode that had an in-game adaptive system that dynamically and continuously adapted its contents according to the student's interactions in the game. In both modes, the interactions between the participants and the game were recorded in a database. The study was performed with 60 students in Higher Education. The results show that the learning style identified by using a learning style questionnaire is not always consistent with the learning style identified in the game. The results also show the learning style fluctuates during the learning process in GBL although there is a tendency for participants to choose the same learning style as the learning style identified outside the game in the first mission of the game. The number of mistakes committed by participants has been identified to have a strong correlation to the fluctuation. The results contribute to the body of empirical evidence in adaptive GBL particularly in identifying the learning style fluctuation in GBL and the paper provides recommendations on the use of adaptivity in GBL to accommodate this fluctuation.     

Exploring the relation between learning style models and preferred multimedia types

There are many adaptive learning systems that adapt learning materials to student properties, preferences, and activities. This study is focused on designing such a learning system by relating combinations of different learning styles to preferred types of multimedia materials. We explore a decision model aimed at proposing learning material of an appropriate multimedia type. This study includes 272 student participants. The resulting decision model shows that students prefer well-structured learning texts with color discrimination, and that the hemispheric learning style model is the most important criterion in deciding student preferences for different multimedia learning materials. To provide a more accurate and reliable model for recommending different multimedia types more learning style models must be combined. Kolb's classification and the VAK classification allow us to learn if students prefer an active role in the learning process, and what multimedia type they prefer.     

The influence of affordances on user preferences for multimedia language learning applications

This study investigates the influence of sensory and cognitive affordances on the user experience of mobile devices for multimedia language learning applications. A primarily audio-based language learning application – ‘Vowel Trainer’, was chosen against a comparison, text and picture-based language learning application – ‘Learn English for Taxi Drivers’. Impressions of the two applications were assessed on two different devices that have virtually the same interface and identical sound output (when headphones are used), but differ in physical size: the iPhone and the iPad. A mixed design was chosen, with native language as a group factor and device type (iPad vs. iPhone) and language application type (audio vs. video) as within groups factors. Assessments of sensory and cognitive affordances were made, along with measurement of learner preferences of each application. Data from 41 participants (21 native English speakers, 20 non-native English speakers) were analysed, revealing device differences in both audio and visual subjective quality ratings, despite only visual quality being affected by the device's physical limitations. We suggest that sensory affordances (indexed by subjective quality) are not simply a function of physical limitations, but are heavily influenced by context. The implications for developing design guidelines for language learning and other multimedia applications are discussed.     

Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral) though the relationships differed between pedagogical agents. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions directed toward specific pedagogical agents (with different roles). Results provide suggestions for adapting pedagogical agents to support learners’ (with certain characteristics; e.g., high in neuroticism or agreeableness) experience of adaptive emotions (e.g., enjoyment) and minimize their experience on non-adaptive emotions (e.g., boredom). Such an approach presents a scalable and easily implementable method for creating emotionally-adaptive, agent-based learning environments, and improving learner-pedagogical agent interactions in order to support learning.     

Learning styles and cognitive traits – Their relationship and its benefits in web-based educational systems

Different learners have different needs; they differ, for example, in their learning goals, their prior knowledge, their learning styles, and their cognitive abilities. Adaptive web-based educational systems aim to cater individual learners by customizing courses to suit their needs. In this paper, we investigate the benefits of incorporating learning styles and cognitive traits in web-based educational systems. Adaptivity aspects based on cognitive traits and learning styles enrich each other, enabling systems to provide learners with courses which fit their needs more accurately. Furthermore, consideration of learning styles and cognitive traits can contribute to more accurate student modelling. In this paper, the relationship between learning styles, in particular the Felder–Silverman learning style model (FSLSM), and working memory capacity, a cognitive trait, is investigated. For adaptive educational systems that consider either only learning styles or only cognitive traits, the additional information can be used to provide more holistic adaptivity. For systems that already incorporate both learning styles and cognitive traits, the relationship can be used to improve the detection process of both by including the additional information of learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model.     

Estimating time and score uncertainty in generating successful learning paths under time constraints

This paper addresses the problem of course (path) generation when a learner's available time is not enough to follow the complete course. We propose a method to recommend successful paths regarding a learner's available time and his/her knowledge background. Our recommender is an instance of long term goal recommender systems (LTRS). This method, after locating a target learner in a course graph, applies a depth‐first search algorithm to find all paths for the learner given a time limitation. In addition, our method estimates learning time and score for all paths. It also indicates the probability of error for the estimated time and score for each path. Finally, our method recommends a path that satisfies the learner's time restriction while maximizing expected learning score. In order to evaluate our proposals for time and score estimation, we used the mean absolute error and average MAE. We have evaluated time and score estimation methods, including one proposed in the literature, on two E‐learning datasets.     

Investigating attributes affecting the performance of WBI users

Numerous research studies have explored the effect of hypermedia on learners' performance using Web Based Instruction (WBI). A learner's performance is determined by their varying skills and abilities as well as various differences such as gender, cognitive style and prior knowledge. In this paper, we investigate how differences between individuals influenced learner's performance using a hypermedia system to accommodate an individual's preferences. The effect of learning performance is investigated to explore relationships between measurement attributes including gain scores (post-test minus pre-test), number of pages visited in a WBI program, and time spent on such pages. A data mining approach was used to analyze the results by comparing two clustering algorithms (K-Means and Hierarchical) with two different numbers of clusters. Individual differences had a significant impact on learner behavior in our WBI program. Additionally, we found that the relationship between attributes that measure performance played an influential role in exploring performance level; the relationship between such attributes induced rules in measuring level of a learners' performance.     

Context counts: How learners' contexts influence learning in a MOOC

Massive Open Online Courses (MOOCs) require individual learners to self-regulate their own learning, determining when, how and with what content and activities they engage. However, MOOCs attract a diverse range of learners, from a variety of learning and professional contexts. This study examines how a learner's current role and context influences their ability to self-regulate their learning in a MOOC: Introduction to Data Science offered by Coursera. The study compared the self-reported self-regulated learning behaviour between learners from different contexts and with different roles. Significant differences were identified between learners who were working as data professionals or studying towards a higher education degree and other learners in the MOOC. The study provides an insight into how an individual's context and role may impact their learning behaviour in MOOCs.     

An ANFIS model of quality of experience prediction in education

This paper presents a Quality of Experience (QoE) prediction model in a student-centered blended learning environment, equipped with appropriate technologically enriched classroom. The model uses ANFIS technique to infer the QoE from the individual subjective factors and the objective technical factors which altogether influence the perceived QoE. We explored the influence of subjective personality traits extroversion and neuroticism, as well as the learning style on QoE. The objective factors included in the model are technically measurable parameters latency, jitter, packet loss and bandwidth affecting Quality of Service (QoS) of the underlying technology. The findings presented in this paper are obtained from a case study which involved 8 teachers and 142 students from second and sixth grade in five primary schools in the Republic of Macedonia. The teachers involved in the project introduced game-based learning strategies in classes, including on-line videoconferences, streamed video content and classical face to face gaming. We constructed three ANFIS systems with seven and four input variables and compared their performances using the RMSE, MAPE and R² measurements. The results showed that perceived QoE can be reliably predicted by the student's personality traits and learning style as subjective factors and network jitter as an objective factor.     

Practical guidelines for designing and evaluating educationally oriented recommendations

There is a need for designing educationally oriented recommendations that deal with educational goals as well as learners' preferences and context in a personalised way. They have to be both based on educators' experience and perceived as adequate by learners. This paper compiles practical guidelines to produce personalised recommendations that are meant to foster active learning in online courses. These guidelines integrate three different methodologies: i) user centred design as defined by ISO 9241-210, ii) the e-learning life cycle of personalised educational systems, and iii) the layered evaluation of adaptation features. To illustrate guidelines actual utility, generality and flexibility, the paper describes their applicability to design educational recommendations in two different contexts, which in total involved 125 educators and 595 learners. These applications show benefits for learners and educators. Following this approach, we are targeting to cope with one of the main challenges in current massive open online courses, which are expected to provide personalised education to an increasing number of students without the continuous involvement of educators in supporting learners during their course interactions.     

Incorporating customization and personalization into game-based learning: A cognitive style perspective

In recent years, customization and personalization were widely applied to accommodate the needs of different cognitive style groups. Such two approaches have different advantages and disadvantages but there is a lack of studies to compare these two approaches from the perspective of game-based learning, which is currently popular in educational settings. To this end, we developed a customized game-based learning system and a personalized game-based learning system and conducted two empirical studies to examine how cognitive styles affected learner's reactions to these two game-based learning systems. The results from the customized game-based learning system showed that Holists might not always favor to listen to music because they frequently switched on/off music. On the other hand, Serialists did not prefer to use hints. In addition, learners with the customized game-based learning system had more positive perceptions while learners with the personalized game-based learning system had more negative perceptions though both systems were useful to enhance learners' learning performance, regardless of their cognitive styles.