شخصیت و اثرات آن بر عملکرد یادگیری: راهنمای طراحی برای یک سیستم آموزش الکترونیکی تطبیقی ​​بر اساس مدل کاربر

An increasingly widespread interest in developing fully adaptable e-learning systems (e.g., intelligent tutoring systems) has led to the development of a wide range of adaptive processes and techniques. In particular, advances in these systems are based on optimization for each user's learning style and characteristics, to enable a personalized learning experience. Current techniques are aimed at using a learner's personality traits and its effect on learning preferences to improve both the initial learning experience and the information retained (e.g., top-down or bottom-up learning organization). This study empirically tested the relationship between a learner's personality traits, analyzed the effects of these traits on learning preferences, and suggested design guidelines for adaptive learning systems. Two controlled experiments were carried out in a computer-based learning session. Our first experiment showed a significant difference in the learning performance of participants who were identified as introverts vs. those who were identified as being extroverts, according to the MBTI scale. As the distinction between extroverted personality types vs. introverted personality types showed the strongest correlation in terms of different learning styles, we used this criteria in our second experiment to determine whether design guidelines for appropriate content organization could reinforce the aforementioned correlation between personality type and learning experience. Relevance to industry: The findings from this article provide how one can practically apply personality traits to the design of e-learning systems. The structure and level of extraversion could be the features to be examined in this regard.

The technological landscape of modern e-learning applications (e.g., adaptive e-learning systems) has advanced due to the availability of new artificial intelligence (AI) algorithms that allow for effective and efficient learning experiences (e.g., Vandewaetere et al., 2011, Papatheocharous et al., 2012). A variety of issues, such as the customization of learning content in computer-based learning activities, serve as the driving forces behind the wide range of adaptive capabilities. Many e-learning applications have been developed to accommodate a certain level of adaptability to an individual's performance based on their usage data, such as how many times they had visited for a particular learning module or which learning process patterns were seen. Machine-learning algorithms have thus been proven to enhance learner satisfaction (e.g., Gerjets et al. 2009), and many studies have now turned their attention to the intrinsic natures of learners (e.g., learning goals, interests, personality, and knowledge level) in order to achieve the best learning experiences (e.g., Brusilovsky, 2001, Germanakos et al., 2008; Vandewaetere, et al., 2011). Pre-emptive algorithms, as compared to reflective machine-learning algorithms, have been widely thought to be promising 21st-century e-learning techniques, as they quickly adapt to a student's learning activities. What is still unknown, however, is which learner characteristics (i.e., the learner's user model) should be collected and how these characteristics should be addressed when designing computer-based learning systems. Early studies (e.g., Riding and Rayner, 1999; Piombo et al., 2003) on learners' usage models claimed that learners have three ontologically distinct features: (i) Personality features, which dictate the student's learning attitude; (ii) Overlay features, which denote the student's current domain knowledge level; and (iii) Cognitive features, which represent the student's information processing characteristics. The last two features have been well studied in instructional design ( Graesser et al., 2007; Graf et al., 2008). It has been postulated that the effects of personality are negligible, since it is the weakest organized set of characteristics possessed by an individual, but primarily because it is thought to be already cemented in his or her cognitive features. However, extensive studies on personality effects (e.g., Germanakos et al., 2008; Honey and Mumford, 1986) have indicated that personality does affect the attitudes and behaviors that determine an individual's preferred way of learning. Therefore, a learner's experience may be significantly altered if the instruction style of an e-learning system were to match their learning style as derived from personality features. Personalization in online education not only facilitates learning through different strategies to create various learning experiences, but it also enables computer-based learning systems to include varied teaching or instructional packages. For example, some studies (e.g., Carver et al., 1999; Vincent and Ross, 2001; Kinshuk and Lin, 2004) identified learner's attitudes, learning goals, interests, and knowledge levels as critical adaptive parameters in personalizing learning content. These researchers assumed that the aforementioned items could be used to determine each learner's cognitive style (Kogan, 1971; Messick, 1970, 1976). Therefore, it is necessary to determine a systematic method of determining a user's cognitive style in advance using relevant attributes. At the same time, the issue of usability has been continually investigated in order to improve e-learning system quality. For example, Barcellini et al. (2009) empirically demonstrated use of a user participatory method in the design process of an e-learning system called ‘Python’. In addition, recent articles have proposed design criteria and objective evaluation scales dedicated to e-learning platforms, including research by Hsu et al. (2009) and Oztekin et al. (2010). More comprehensively, Brusilovsky (2001) proposed seven attributes for use in user models of adaptive e-learning systems, as shown in Fig. 1: learners' backgrounds, knowledge, goals/tasks, previous learning experience, preferences, interests, and interaction style. This model showed a significant impact on subsequent user modeling activities for personalizing adaptive e-learning systems. However, Jungian-based psychologists have contended that people's personality preferences influence the way they may or may not want to become more actively involved in their learning activities, as well as whether they take responsibility for self-direction and discipline (e.g., Felder et al., 2002; Soles and Moller, 2001). Following a similar line of thought, several researchers (e.g., Gilbert and Han, 1999; Kwok and Jones, 1985; Papanikolaou et al., 2002; Moallem, 2003;) tried to integrate learning style into an adaptive application, matching personal learning style with an appropriate instruction design in order to adapt to that person's strengths and preferences; however, these researchers did not attempt to examine personality effects. Therefore, the goal of this study was to examine the inclusion of a learner's personality features in a user model. The findings were then applied to learning materials, which were empirically tested. This paper is organized as follows. In Section 2, we reviewed the possible relationships between a learner's personality and the learning styles included in the user model of adaptive learning systems. In Sections 3 and 4, we examined the personality effect in adaptive e-learning systems. Our first experiment explored the relationship between different personality traits and their effects on learning performance. The second experiment determined whether personality differences can serve as an appropriate criterion for designing an e-learning system that best suits a learner's strengths and preferences, thereby connoting the personality effect in the user model. Finally, in Section 5, we discuss our empirical findings, as well as several design guidelines for adaptive learning systems.

This article examined how personality traits might be applied to the design of e-learning systems in order to provide a pragmatic approach to user modeling for practitioners. The first experiment demonstrated that the level of extraversion could be such a feature. Based on this, Experiment II intended to empirically demonstrate an approach to incorporating personality traits in the design of structured learning content, which is a novel contribution. 5.1. Embodying personality in the user model The diagram shown in Fig. 7 illustrates that personality traits can mediate between the user model and the instructional design, perhaps dictating the appropriate structure and content. An understanding of this relationship would allow for the practical design of computer-based learning systems based on user personality. Even though the Big Five theory (Costa and McCrae, 1992) is the more academically dominant approach, rather than MBTI, our study was based on MBTI, which has been commercially successful and which simplifies the definition of the learning styles of each individual learner. It is therefore suggested that designers of computer-based learning systems could benefit from the practicality of MBTI, rather than employing the Big Five theory. Our findings regarding the relationship between user modeling and personality traits are not new. In particular, Wicklein and Rojewski (1995) claimed that a better understanding of personality could lead to improved satisfaction of individual learning needs and could allow educators to provide an optimal learning environment. Lauridsen (2001) further contended that adaptive e-learning systems should focus not only on technologies, but also on learning styles and personal approaches. However, the user models employed in most adaptive e-learning systems do not consistently apply specific content or support for students' learning activities in terms of different knowledge acquisition strategies. Additionally, we sought to provide practical guidance for designing e-learning experiences optimized for individual learners' personalities. Our personalized user model can monitor adaptive effects, so as to refine itself for subsequent adaptations (in this study, in terms of the structure and content of the learning materials). 5.2. Using the results and further research The empirical data presented in Section 4 supports the feasibility of implementing a personality-adaptive e-learning system and some insights as to why ours might be more effective in some circumstances. One possible approach to designing ‘personality-adaptive’ e-learning systems would be the development of content sequencing congruent with the personality traits of the learner. However, we have not yet applied this approach in real computer-based learning systems, and thus the success of this method is another empirical question for further study. No user modeling for adaptive e-learning systems can encompass all design issues. This study addressed personality level, determined according to MBTI standards, for both user preferences and goals as assumed based on MBTI temperaments, and thus incorporated personality, which we believe to be a less-examined but crucial factor. Hence, the same results may not be observed with other personality theories (such as clinical personality theory, HDM – Human Dimensions Model ( Khan and Radcliffe, 2005)) or other tutoring systems with the same experimental contexts. The major limitation of this empirical study may be that the participants were only computer science students, and none were students of other disciplines which may have given more diverse personality types ( Bayne, 2004). For example, according to Myers and McCaulley (1985, p. 42), almost 90% of fine art students were found to be intuitive, compared with 32% of the general US population. In addition, 86% of a sample of US independent studies students preferred feeling–perceiving, compared to 28% of the general population. Another question is how this approach will scale up to complex, real e-learning system design projects. As mentioned in the introduction, computer-based e-learning systems should be cost effective and easy to use by teachers and educational providers. Considering personality traits would require extensive effort, which may not be pragmatic outside the confines of a research study. To confirm that this approach is practical, further large-scale studies will be required.