Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

Wang, Ling; Hu, Gongliang; Zhou, Tie Hua

doi:10.3390/su10061921

Cited by 55 publications

(45 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These feelings generated by the interaction (comments, likes, etc.) have a relation with the activity of the students in the MOOC [25]. Within this point of view, it is interesting how the participants are predisposed to give "likes" but only 37% are motivated to receive them.…”

Section: Discussionmentioning

confidence: 99%

“…This will enable students to improve their skills and knowledge [24] by devoting more time to study and with greater involvement. It should not be forgotten that emotions and feelings [6,25] have a great impact on learning processes, generating greater engagement or on the contrary even reaching frustration within the course, it will be important to take them into account for proper management.…”

Section: Gamification Principlesmentioning

confidence: 99%

“…The use of digital social networks in education as VLC is becoming more widespread, given the impact it has on learning, with aspects such as motivation, engagement, satisfaction, and interaction [27] enabling online sharing and collaboration. Another aspect to study is the feelings generated when an interaction takes place, through discussions or comments in these networks, which is an important element when related to the activity of users in the MOOC [25].…”

Section: Gamification and Vlcmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Fun through Gamification to Improve Engagement in MOOC

2019

View full text Add to dashboard Cite

Massive Open Online Courses (MOOCs), regardless of their topic, are a perfect space to generate, through virtual learning communities associated with them, very valuable resources for their participants and, in general, anyone interested in the topic covered. If in the design of these learning spaces, elements specific to games are added to them, which is known as gamification, we can try to increase the engagement of the student towards the course and, therefore, towards the community. This paper presents an experience of a MOOC of Universidad Rey Juan Carlos (Spain) with a connectivist approach. Aspects such as fun and motivation have been worked on in the design, through the application of gamified activities and the use of elements from social networks, considered as gamification, with the aim of increasing participation and engagement within a Facebook group, used as a community to support the course. We have analyzed aspects such as enjoyment and motivation, the result of which has been active participation and high engagement within the MOOC community in the form of content and especially great interaction, highlighting the existence of continuous activity once the edition of the MOOC is finished, as a consequence of a habit generated in the student.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Gamification Principlesmentioning

confidence: 99%

Section: Gamification and Vlcmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Fun through Gamification to Improve Engagement in MOOC

2019

View full text Add to dashboard Cite

show abstract

“…Sequential emotion pattern is suitable for identifying mental disorders because except for the factors of environment and personality, these are mainly surrounded with shifting or repeating one or few distinguishable emotions [29,30]. Researches show that emotion from social media can better interpret the symptoms of mental disorder [31][32][33][34].…”

Section: Discussionmentioning

confidence: 99%

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

2020

Self Cite

View full text Add to dashboard Cite

Mental disorder has been affecting numerous individuals; however, mental health care is in a passive state where only a minority of individuals actively seek professional help. Due to the rapid development of social networks, individuals accustomed to expressing their raw feelings on social media include patients who are suffering great pain from mental disorders. To distinguish individuals who merely feel sad and others who have mental disorders, the symptoms of mental disorder are taken into consideration. These symptoms constantly arise as a regular pattern like shifting of emotions or repeating of one representative emotion during a certain time. We proposed a Mental Disorder Identification Model (MDI-Model) to identify the four most commonly occurring mental disorders in the world: anxiety disorder, bipolar disorder, depressive disorder, and obsessive-compulsive disorder (OCD). The MDI-Model compares the sequential emotion pattern from users to identify mental disorders to detect those who are in a high risk. Tweets of diagnosed mental disorder users were analyzed to evaluate the accuracy of the MDI-Model, furthermore, the tweets of users from six different occupations were analyzed to verify the precision and predict the tendency of mental disorder among the different occupations. Results show that the MDI-Model can efficiently diagnose users with high precision in different mental statuses as severe, moderate, and mild stage, or tendency of mental disorder and mentally healthy status.

show abstract

“…Many studies further apply emotional analysis to different domains. Wang, et al [30] studied the emotional tendency in Massive Open Online Courses (MOOCs). They tracked the learner's emotion through the semantic analysis model.…”

Section: Emotional Analysismentioning

confidence: 99%

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

Gao

et al. 2020

Sustainability

View full text Add to dashboard Cite

(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

show abstract

Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

Cited by 55 publications

References 42 publications

Enhancing Fun through Gamification to Improve Engagement in MOOC

Enhancing Fun through Gamification to Improve Engagement in MOOC

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

Contact Info

Product

Resources

About