Big Five Personality in Online Learning and Games

2016 ◽  
Vol 7 (3) ◽  
pp. 33-46 ◽  
Author(s):  
Peter Krátky ◽  
Jozef Tvarožek ◽  
Daniela Chudá
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Learning Style ◽  
Individual Student ◽  
Online Learning Environment ◽  
Big Five Personality ◽  
Effective Learning ◽  
Learning Content ◽  
Student’S Performance ◽  
Programming Courses

Online learning gives promise of effective learning for masses. Personalized learning experiences tailored for individual needs and preferences of each student are key ingredients in making online learning successful. Current approaches to adaptive and personalized online learning use student's personality profile and preferred learning style to adapt learning content and activities in order to provide the best possible experience to each individual student. Research has shown that effects on different types of learning activities in various settings may be different. This study analyses how personality affects student's performance in an online learning environment for programming exercises and how the student's personality can be estimated unobtrusively using a casual online game. The data used to evaluate were collected from an online learning environment used in university programming courses over the course of several years. The activity indicators show significant correlations with overall academic results of students and particularly with personality traits.

Pre-Service Teachers Engaging with Twitter as a Professional Online Learning Environment

2016 ◽  
pp. 1434-1464
Author(s):  
Narelle Lemon
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Online Learning ◽  
Learning Environment ◽  
Peer Interactions ◽  
Online Learning Environment ◽  
Learning Content ◽  
Teachers And Students ◽  
Over Time

New ways of utilizing technology in the online space are challenging different ways teachers and students can interact with each other and learning content. Social media is one such technology that is a flexible and powerful tool in higher education; however, as yet, it is still under-researched. Twitter challenges notions of public global dialogue, continuous discussions in the online space beyond the four walls of a physical classroom, and the role of peer-to-peer interactions. This chapter discusses a project that aimed to address the need to understand more deeply what happens pedagogically in the classroom when integrating Twitter into learning activities. The case shared is of one undergraduate second-year class located in Teacher Education. The change over time with students' ability to professionally engage with Twitter demonstrated a shift in being able to confidently participate and critically think about this social media as a valuable online learning environment.

An Investigation of the Effect of Learning Style on Student Success in an Online Learning Environment

2000 ◽  
Vol 28 (3) ◽  
pp. 231-238 ◽  
Author(s):  
Steven R. Terrell ◽  
Laurie Dringus
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Graduation Rate ◽  
Information Science ◽  
Demographic Data ◽  
Drop Out ◽  
Online Learning Environment ◽  
Learning Style Inventory

Ninety-eight information science students were tracked during an online masters degree program. At their initial orientation, each student completed a demographic data form and the Kolb Learning Style Inventory. Because of their major, it was hypothesized that students would fall into Kolb's Converger and Assimilator categories and these learning styles would be predictive of success in the program. Results indicated that 79.6 percent ( n = 78) of all students graduated from the program. Seventy-three students (74.5%) fell into the predicted categories and maintained an 83.6 percent ( n = 61) graduation rate. Students not falling into the predicted categories maintained a 68 percent ( n = 17) graduation rate. The implications are clear. First, the majority of students can succeed in an online learning environment regardless of their learning style. Care has to be taken, however, since a trend existed in this study for students with learning styles different from predicted to drop out in higher numbers. Institutions offering online programs should be aware of this and be prepared to address learning style issues.

scholarly journals LEARNING STYLE DETECTION USING K-MEANS CLUSTERING

2020 ◽  
Vol 4 (3) ◽  
pp. 375-381
Author(s):  
Mubaraka Sani Ibrahim
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Learning Styles ◽  
Unsupervised Learning ◽  
Learning Style ◽  
Educational System ◽  
Learning Experience ◽  
Experimental Results ◽  
Online Learning Environment ◽  
Learning Methods

Learning theorist established the fact that learners are characterized according to their distinct learning styles. Investigating learners' learning style is important in the educational system in order to provide adaptivity and improve learning experience. Past researches have proposed various approaches to detect learning styles. Among unsupervised learning methods, the K-means clustering has emerged as a widely used method to predict patterns in data because of its simplicity. This paper evaluates the performance of K-means clustering in automatically detecting learners’ learning style in an online learning environment. The experimental results prove differences in learning thus characterizing learners based on learning style.

Pre-Service Teachers Engaging with Twitter as a Professional Online Learning Environment

2015 ◽  
pp. 204-234 ◽  
Author(s):  
Narelle Lemon
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Online Learning ◽  
Learning Environment ◽  
Peer Interactions ◽  
Online Learning Environment ◽  
Learning Content ◽  
Teachers And Students ◽  
Over Time

New ways of utilizing technology in the online space are challenging different ways teachers and students can interact with each other and learning content. Social media is one such technology that is a flexible and powerful tool in higher education; however, as yet, it is still under-researched. Twitter challenges notions of public global dialogue, continuous discussions in the online space beyond the four walls of a physical classroom, and the role of peer-to-peer interactions. This chapter discusses a project that aimed to address the need to understand more deeply what happens pedagogically in the classroom when integrating Twitter into learning activities. The case shared is of one undergraduate second-year class located in Teacher Education. The change over time with students' ability to professionally engage with Twitter demonstrated a shift in being able to confidently participate and critically think about this social media as a valuable online learning environment.

Competence-based learning in engineering mechanics in an adaptive online learning environment

2018 ◽  
Vol 38 (3) ◽  
pp. 146-153
Author(s):  
Markus Linke ◽  
Karin Landenfeld
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Learning Strategy ◽  
Individual Student ◽  
Computer Assisted ◽  
Online Learning Environment ◽  
Learning Path ◽  
Engineering Sciences ◽  
Engineering Mechanics ◽  
The Individual

Abstract In engineering sciences like mechanical or automotive engineering, pre-knowledge in mathematics is required to follow deeply specific engineering lectures. Mathematical skills are essential preconditions to successfully complete engineering lectures. It is necessary that these preconditions are identified for each individual student so that suitable learning opportunities can be provided according to the individual student needs. Within this paper an approach of competence-based learning in engineering mechanics is presented. This approach is assisted by an online learning environment that is adapted and extended by several features in order to enable a competence-oriented learning strategy. Computer-assisted tests are used for measuring mathematical pre-knowledge and pre-skills. Moreover, a mastery learning approach based on exercises is utilized in order to secure a certain skill level before students move forward to learn subsequent competencies. Test results influence the individual learning path by offering different learning elements to each student.

Coordination and Teacher Development in an Online Learning Environment

2013 ◽  
Vol 23 (3) ◽  
pp. 551-568 ◽  
Author(s):  
Pauline Ernest ◽  
Joseph Hopkins
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Teacher Development ◽  
Online Learning Environment

Improving the Asynchronous Online Learning Environment Using Discussion Boards

2015 ◽  
Vol 12 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Ilene Ringler ◽  
◽  
Carol Schubert ◽  
Jack Deem ◽  
Jimmie Flores ◽  
...  
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Online Learning Environment ◽  
Discussion Boards ◽  
Asynchronous Online Learning
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