Using Social Media to Promote STEM Education: Matching College Students with Role Models

Ling, He; Murphy, Lee; Luo, Jiebo

doi:10.1007/978-3-319-46131-1_17

Cited by 15 publications

(15 citation statements)

References 21 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The career role models who share similar interests and demographics were argued to help inspire students to develop interests in STEM-related fields (He, Murphy, & Luo, 2016). The students will have the ambition to emulate the role models.…”

Section: Role Model/mentoringmentioning

confidence: 99%

“…Social media can act as a medium. Twitter college student users and diverse LinkedIn STEM professionals matching using a ranking algorithm based on the similarities of their demographics and interests have been proposed to promote STEM education (He et al, 2016). The results of evaluations on 2000 real college students indicated that the algorithm can accurately match both parties.…”

Section: Role Model/mentoringmentioning

confidence: 99%

“…Prospective students who show interest in the STEM and TVET subjects will be matched with a role model or mentor using a matching algorithm. Their interactions could be using suitable social media such as LinkedIn (He et al, 2016). The role model or mentor could be the lecturers or successful alumni from STEM and TVET courses of higher educational institutions.…”

Section: Role Model / Mentoringmentioning

confidence: 99%

“…Recently, the promotion of Science, Technology, Engineering and Mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce (He, Murphy, & Luo, 2016). Furthermore, the enrolment rates in STEM degrees are still reported to be low in many European countries (Achilleos, Mettouris, Yeratziotis, Papadopoulos, Pllana, Huber, & Dinnyés, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Adnan

Ariffin

Hanafi

et al. 2021

AJUE

View full text Add to dashboard Cite

Recently, the promotion of Science, technology, engineering and mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce. Surprisingly, the enrolment rates in STEM degrees are still low in many countries. Social media has been identified as one of the main platforms that can help to increase prospective students’ interest in STEM and also Technical and Vocational Education and Training (TVET) subjects. However, very little research has been done for the higher education institutions in Malaysia in leveraging social media and social media analytics effectively to increase the students’ interests and awareness of STEM and TVET disciplines. Therefore, this paper aims to propose a framework to increase prospective students’ interest in STEM and TVET using social media and big data analytics. The objectives of this study are to explore various social media applications in education and study these applications towards increasing students’ interests and propose a suitable framework for Malaysian higher education institutions. The framework is proposed by following the theory synthesis methodology. Four main components of the framework have been proposed, namely social media, role model or mentoring, massive open online courses and big data analytics. Each component is significant and requires a considerable amount of time to develop. The suggested framework is anticipated to benefit higher education institutions with a significant gain of the number of students, revenues and positive reputations. Keywords: Social media, Social media analytics, STEM, E-learning, Education

show abstract

Section: Role Model/mentoringmentioning

confidence: 99%

Section: Role Model/mentoringmentioning

confidence: 99%

Section: Role Model / Mentoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Adnan

Ariffin

Hanafi

et al. 2021

AJUE

View full text Add to dashboard Cite

show abstract

“…In the future, we may consider ways to identify the occupations of the face owners to investigate the distribution over different occupations. A recent study [16] has proposed a way to identify if a Twitter user is a college student or not. This method may help us unveil more interesting fatigue patterns of the student population.…”

Section: Limitation and Future Workmentioning

confidence: 99%

Sleep-deprived fatigue pattern analysis using large-scale selfies from social media

Peng

Luo

Glenn

et al. 2017

2017 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

The complexities of fatigue have drawn much attention from researchers across various disciplines. Shortterm fatigue may cause safety issue while driving; thus, dynamic systems were designed to track driver fatigue. Longterm fatigue could lead to chronic syndromes, and eventually affect individuals physical and psychological health. Traditional methodologies of evaluating fatigue not only require sophisticated equipment but also consume enormous time. In this paper, we attempt to develop a novel and efficient method to predict individual's fatigue rate by scrutinizing human facial cues. Our goal is to predict fatigue rate based on a selfie. To associate the fatigue rate with user behaviors, we have collected nearly 1-million timeline posts from 10,480 users on Instagram. We first detect all the faces and identify their demographics using automatic algorithms. Next, we investigate the fatigue distribution by weekday over different age, gender, and ethnic groups. This work represents a promising way to assess sleepdeprived fatigue, and our study provides a viable and efficient computational framework for user fatigue modeling in largescale via social media.

show abstract

Large-Scale Sleep Condition Analysis Using Selfies from Social Media

Peng

Luo

Glenn

et al. 2017

Social, Cultural, and Behavioral Modeling

Self Cite

View full text Add to dashboard Cite

Sleep condition is closely related to an individual's health. Poor sleep conditions such as sleep disorder and sleep deprivation affect one's daily performance, and may also cause many chronic diseases. Many efforts have been devoted to monitoring people's sleep conditions. However, traditional methodologies require sophisticated equipment and consume a significant amount of time. In this paper, we attempt to develop a novel way to predict individual's sleep condition via scrutinizing facial cues as doctors would. Rather than measuring the sleep condition directly, we measure the sleep-deprived fatigue which indirectly reflects the sleep condition. Our method can predict a sleep-deprived fatigue rate based on a selfie provided by a subject. This rate is used to indicate the sleep condition. To gain deeper insights of human sleep conditions, we collected around 100,000 faces from selfies posted on Twitter and Instagram, and identified their age, gender, and race using automatic algorithms. Next, we investigated the sleep condition distributions with respect to age, gender, and race. Our study suggests among the age groups, fatigue percentage of the 0-20 youth and adolescent group is the highest, implying that poor sleep condition is more prevalent in this age group. For gender, the fatigue percentage of females is higher than that of males, implying that more females are suffering from sleep issues than males. Among ethnic groups, the fatigue percentage in Caucasian is the highest followed by Asian and African American.

show abstract

Using Social Media to Promote STEM Education: Matching College Students with Role Models

Cited by 15 publications

References 21 publications

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Sleep-deprived fatigue pattern analysis using large-scale selfies from social media

Large-Scale Sleep Condition Analysis Using Selfies from Social Media

Contact Info

Product

Resources

About