The Effect of an Online Teaching Platform on Self-concept and Self-regulation of Medical Students at Kashan University of Medical Sciences


avatar Maryam Najafi ORCID 1 , avatar Mohammad Ali Heidari Shahreza 1 , * , avatar Saeed Ketabi 2

Department of English, Islamic Azad University, Shahreza Branch, Shahreza, Iran
Department of English, University of Isfahan, Isfahan, Iran

how to cite: Najafi M, Heidari Shahreza M A , Ketabi S . The Effect of an Online Teaching Platform on Self-concept and Self-regulation of Medical Students at Kashan University of Medical Sciences. Educ Res Med Sci. 2022;11(2):e121396.



Online language learning has recently gained a reputation in educational research, holding specifically true in English for specific purposes (ESP) courses. However, ESP online learning has not been sufficiently examined in particular disciplines such as medicine.


This study investigated the effects of teaching English medical vocabulary through a virtual learning platform (Adobe Connect) on Iranian medical students’ learner factors, including their self-regulation and self-concept.


This quasi-experimental study was conducted on 60 female and male medical students learning English for Medicine at Kashan University of Medical Sciences in 2021. An Oxford Placement Test (OPT) was administered to ensure the sample’s homogeneity. Then, the participants were categorized into Adobe Connect (experimental) and conventional face-to-face (control) groups. Data were collected using the Academic Self-concept Questionnaire (ASCQ) and the Self-regulation Questionnaire (SRQ). An ANCOVA test was run to compare the possible role of the two instructional methods on medical students’ self-regulation and self-concept.


Adobe Connect, as the experimental group, had a higher median score than the control group regarding the self-regulation posttest (P = 0.000). Therefore, using Adobe Connect virtual platform significantly improved the self-regulation of ESP medical students. Additionally, the results revealed that the experimental participants outperformed the control group regarding their self-concept (P = 0.000).


Based on the results, the online platform positively affected self-regulation and self-concept among medical students.

1. Background

Digital English language learning has recently gained primary importance in the extensive field of education and assessment as the best option to prevent epidemics (1). Furthermore, traditional education has changed to educational technologies, where teaching and assessments are conducted online. Most users of e-learning platforms believe that e-learning platforms can be easily managed. The student can easily access the instructors and teaching materials (2). The online platforms seem influential in English for specific purposes (ESP) courses. Medical sciences, as one branch of ESP, faced various difficulties during the COVID-19 pandemic in terms of instruction. Medical students are similar to other learners who should experience distance and digital learning. Some studies have been conducted in this era, and there has been growing interest in digital English language learning and individual learner factors such as self-regulation, self-concept, and motivation (3, 4). Scholars believe that such issues could have desirable roles in preparing online resources for English language skill learning and enhancing students’ affective and personality traits (5, 6). These resource-based innovations offered new avenues in curriculum design to incorporate updated forms of communicative digital learning tools (7, 8). Adobe Connect software is a digital learning platform that enhances learners’ skill performance and improves their self-regulation and self-concept in English classes. This platform has been more centralized during the COVID-19 pandemic as a reaction to the absence of conventional face-to-face classrooms. Adobe Connect has several unique features, such as users’ ability to engage with various audiences worldwide. This internet-based platform was a suitable cure for worries regarding online learning instruments and settings to proceed with second language (L2) education. Interestingly, such online applications satisfy students’ key needs and help improve their autonomy, voice, and genre awareness (3, 9).

Self-concept is closely related to educational aspects among learner factors. Generally, self-concept is an individual’s perception of themselves and how knowledge is developed and evaluated based on personal experiences (10). Moreover, self-concept and academic achievement are mutually supportive, so each modification changes the other (11).

The literature has considered self-concept and self-esteem synonymous. This study assessed self-concept as the cognitive and knowledge-based views on one’s experiences (12). Typically, self-concept develops at eight years old in humans. Primarily, a child tries to make sense of their mental operations, emotions, and capacities, followed by interpreting surrounding feedback (13). This process occurs due to social communications and comparisons (14). Although considerable research has been conducted on students’ academic self-concept, motivation, and academic achievement (15-19), self-concept has not received adequate attention, especially in a digital learning context.

In addition to self-concept, self-regulation has been well-recognized in previous studies. Self-regulated learning (SRL) is one of the domains of self-regulation, which is aligned most closely with educational aims and refers to learning guided by metacognition (thinking about one’s thinking), strategic action (planning, monitoring, and evaluating personal progress against a standard), and motivation to learn. Creating active and efficient learners has been considered an important educational goal. This goal is accomplished through using SRL strategies by learners (20). Self-regulation plays a crucial role in L2 learning flourish. Dörnyei stated that self-regulation includes an array of cognitive, metacognitive, motivational, behavioral, and environmental aspects that academically help learners improve across contexts (21). Psychologically, SRL involves learners in a goal-oriented process of managing, fostering, and evaluating their learning (22). In L2 education, a bulk of research reveals the primary role of SR strategies in promoting students’ language proficiency (23-25).

English for specific purposes contexts, however, have not paid much attention to developing digital tools to promote self-regulation in learners. Incorporating computer-based instruction or digital learning is critical as learners respond positively to this teaching context regarding cognitive and social aspects. There are more instances of this issue when traditional classrooms are unavailable, such as in the COVID-19 era. Computer-assisted language learning (CALL) has significantly changed the learning-teaching cycle due to technological advancements, such as modern digital devices and extensive learner network connections. In recent years, the popularity of CALL resulted in an increased number of online course offerings by schools and colleges (26). In addition, technological advancement and student demand for online classes have influenced colleges and universities to implement online courses for the students (27).

2. Objectives

This study examined the impact of teaching technical medical vocabularies through Adobe Connect on medical students’ learner factors, including self-concept and self-regulation in an ESP context.

3. Methods

The target sample in this quasi-experimental study was students of medicine studying at Kashan University of Medical Sciences, Kashan, Iran. As explained in the published article (28), 60 medical students were chosen due to the homogeneity in general English knowledge using the Oxford Quick Placement Test (OQPT) from among 75 medicine students in an Advanced English course. The OQPT had 60 items to assess English skills and subskills, and the students should complete the test in 45 minutes. Based on the homogeneity test, 60 participants were comparable and randomly divided into Adobe Connect (n = 30) and traditional (n = 30) groups.

A similar study’s standard deviation and mean values (28) were used based on the sample size formula for comparing two means to account for the sample size. Each group was estimated to have 27 students with 99% confidence and 90% power. A 10% loss of the samples was considered and therefore 30 students were included in each group (total = 60).

In this study, the scale developed by Liu and Wang was used to assess students’ self-concept before and after the treatment (29). The questionnaire included 20 items on a 6-point Likert scale from no to yes always ((1 (no), 2 (no always), 3 (no sometimes), 4 (yes), 5 (yes sometimes), and 6 (yes always)). As Liu and Wang found, the internal consistency of the questionnaire based on Cronbach’s alpha coefficient was 0.82 (29). Furthermore, Minchekar reported a Cronbach’s alpha coefficient of 0.92 (30). Moreover, the Self-regulation Questionnaire (SRQ) scale developed by Brown et al. was employed as the other data collection tool. The questionnaire had 63 items on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree” (31). The Cronbach’s alpha reliability coefficient of the questionnaire was estimated to be 0.91, 0.92, 0.84, and 0.86 in four relevant studies (31-34). The two questionnaires were distributed among the medical students in both groups before and after the intervention. A comparison was made between the students’ levels of self-concept and self-regulation after participating in Adobe Connect or traditional classrooms.

The students were pretested using self-regulation and self-concept questionnaires one week before the study. After this phase, the intervention started, mostly around vocabulary learning, and lasted eight sessions (90 min in each session). During each session, ten new medical English words were taught. The participants in the experimental group were conducted using the Adobe Connect virtual platform. The instructor (the first researcher) implemented diverse online activities in the virtual classroom, such as lectures and recordings. In addition, the instructor could share her computer screen with the students and provide different materials. The respective course book was “Advanced English in Medicine,” mainly focusing on new medical English vocabulary students needed to learn for future uses. During each session, ten new words were selected from the course book texts and were delivered and practiced via slides. The teacher highlighted the new words’ pronunciation, part of speech, definition, synonyms, and collocations and also encouraged the students to add related words and type them in a chat box or on the platform whiteboard and make sentences. Peer work was encouraged for meaning-making among students.

Most universities in different countries, including Iran, banned face-to-face classes during the COVID-19 pandemic. However, this study was conducted when most of the students had received the first dose of the COVID-19 vaccine and agreed to participate in the study. The control participants were given the exact words each session. The procedure for the controls was the same as the experimental group. Both groups received the same instructional method, the same number of new words, and the same amount of time. The only difference lay in the instructional setting, which was virtual in the Adobe Connect group and face-to-face in the control group. The intervention lasted eight sessions, and two weeks after the intervention, the self-concept and self-regulation questionnaires were administered to examine the effect of teaching through Adobe Connect on these factors in both groups.

The data were analyzed by SPSS software version 22 (SPSS, Inc., Chicago, IL, USA). The significance level was considered at P < 0.05.

4. Results

The mean age of the participants was 24.95 ± 5.3 years (range, 21 - 29 years). A total of 36 students (60%) were females, and the rest were males. There was a significant difference in the mean age of the participants between the experimental (24.21 ± 2.11) and control (22.24 ± 2.12) groups (P = 0.001). Consequently, the researchers took advantage of the ANCOCA test for the study’s data analysis. More specifically, this test was used to determine whether participants’ self-concept and ability to regulate themselves were affected by their age and treatment (Table 1).

Table 1.

Descriptive Statistics for the Medical Students’ Performance on the Self-concept Posttest

GroupsMean ± Standard Deviationn
Experimental group101.30 ± 6.47130
Control group83.53 ± 3.76730
Total92.42 ± 10.38360

Levene’s test examined the equivalence of variance values between groups. Based on the test results, the variance values were equivalent (P = .48). Therefore, the researcher examined the homogeneity of the regression slopes (Table 2).

Table 2.

Homogeneity Test of Regression Slopes of the Participants’ Performance on the Self-concept Posttest

SourceType III Sum of SquaresdfMean SquareFP-ValuePartial Eta Squared
Corrected model4747.12731582.37654.9210.0000.746
Groups × age11.660111.660.4050.5270.007
Corrected total6360.58359

As shown in Table 2, the interaction between the independent variable (i.e., Adobe Connect treatment) and the covariate (i.e., age) was not significant (P = 0.527). Consequently, the researcher examined the difference between the performances of the experimental group and the control group on the self-concept posttest (Table 3).

Table 3.

Comparison Between the Performances of the Experimental Group and Control Group on the Self-concept Posttest

SourceType III Sum of SquaresdfMean SquareFP-ValuePartial Eta Squared
Corrected model4735.467 a22367.73383.0470.0000.745
Corrected total6360.58359

Table 3 shows a significant difference between the experimental and control group’s performances on the self-concept post-test (P = 0.000).

Moreover, the researcher used the ANCOVA test to examine the interaction between the participant’s age and treatment on their self-regulation (Table 4).

Table 4.

Descriptive Statistics for the Medical Students’ Performance on the Self-regulation Posttest

GroupsMean ± Standard Deviationn
Experimental group278.93 ± 13.22230
Control group212.40 ± 8.70830
Total245.67 ± 35.33660

The researcher examined the results of Levene’s test to determine the equivalence of variances between the groups. Based on the test results, the variance values were equivalent (P = 0.21). Therefore, the researcher examined the homogeneity of the regression slopes (Table 5).

Table 5.

Homogeneity Test of the Regression Slopes Regarding the Participants’ Performance on the Self-regulation Posttest

SourceType III Sum of SquaresdfMean SquareFP-ValuePartial Eta Squared
Corrected model66571.863 a322190.621175.0870.0000.904
Groups × age118.8941118.8940.9380.3370.016
Corrected total73669.33359

As shown in Table 5, the interaction between the independent variable (i.e., Adobe Connect treatment) and covariate (i.e., age) was not statistically significant (P = 0.337). Therefore, the researcher examined the difference between the performances of the experimental group and the control group on the self-regulation posttest (Table 6).

Table 6.

Comparison Between the Performances of the Experimental Group and the Control Group on the Self-regulation Posttest

SourceType III Sum of SquaresdfMean SquareFP-ValuePartial Eta Squared
Corrected model66452.969 a233226.485262.4470.0000.902
Corrected total73669.33359

Table 6 indicates a significant difference between the experimental and control group’s performances on the self-regulation post-test (P = 0.000).

Consequently, the Adobe Connect-based vocabulary instruction was more effective for ameliorating the participants’ self-concept and self-regulation than the traditional vocabulary instruction.

5. Discussion

This study aimed to evaluate the teaching effectiveness through an online platform, such as Adobe Connect, in affecting Iranian ESP medical students’ self-regulation and self-concept. The results revealed that the Adobe Connect group’s self-regulation scores were higher than the controls. Thus, online learning instruction significantly improved self-regulation among the students. This finding agrees with previous studies, highlighting the superiority of technology-enhanced learning environments in promoting self-regulated learning (35-37). Students’ ability to use computers, information technology, and the internet is attributed to the effectiveness of online instruction in the Iranian context, where self-study is a dominant practice. As echoed by Zimmerman and Schunk, self-regulation among students is closely associated with contextual factors (38). In other words, self-regulation is promoted when teachers foster learner engagement and interact with their students (39).

The learning outcomes of SRL-oriented classes are enhanced, particularly in terms of the vocabulary component. As Bernacki et al. concluded, self regulation practices positively affect learning and development (40). Furthermore, Seker showed that self-regulated learners could set their learning objectives and build knowledge independently, especially in the case of learning English in online learning classrooms (37). The evidence for the claim can be Orhan et al.’s study, which implied the less effectiveness of traditional classes in preparing self-regulated learners (41). Digital learning can be advantageous in terms of its appropriacy in accounting for various learning paces. In a digital learning environment, students are fully responsible for their learning and control of the process (42). Kassab et al. argued that self-regulation is not a fixed trait; motivation and learning strategies of the students may be improved when practical and interactive online instruction are provided (43).

According to Barnard et al., online learning affects cognitive, metacognitive, and motivational aspects and improves self-regulation (44). However, many SRL practices are required in online classes, and less self-regulated learners may encounter numerous challenges. On the contrary, virtual classes can be suitable for learners with high potential and self-control abilities (44). However, it is unclear whether struggling learners can deal with online learning challenges. Any virtual learning platform could be helpful depending on design issues, such as learning tasks and information. The features of online learning platforms that support self-regulation should be considered when developing such environments.

Additionally, this study investigated the effect of teaching medical vocabularies through Adobe Connect on students’ self-concept, and the findings approved the effectiveness of the online platform in this respect. A virtual class like Adobe Connect would greatly benefit from considering student attitudes toward learning in light of our results. In line with this finding, Alexander found a significant relationship between academic performance and self-concept in an online learning platform (45). On the other hand, the results differ from those of Zhan and Mei, who reported an insignificant difference between online and traditional instruction in affecting self-concept and academic learning outcomes (46). In the same vein, previous studies have found no significant differences between traditional classroom instruction and distance education regarding self-concept (47, 48). Both methods require a strong sense of academic self-concept.

5.1. Conclusions

The results revealed that medical vocabulary instruction through Adobe Connect significantly improved medical students’ learner factors, including their self-regulation and self-concept. Therefore, digital learning tools can help deal with learning and teaching challenges during the COVID-19 pandemic. Education has changed dramatically under the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. Major world events are often an inflection point for rapid innovation in the big field of e-learning. Although Adobe Connect virtual classrooms can be used globally during the COVID-19 pandemic and post-virus era, face-to-face classes should not be overlooked. The pandemic has pushed the teaching industry worldwide to find alternatives to in-person instruction.



  • 1.

    Lizcano D, Lara JA, White B, Aljawarneh S. Blockchain-based approach to create a model of trust in open and ubiquitous higher education. J Comput High Educ. 2019;32(1):109-34.

  • 2.

    Adeshola I, Agoyi M. Examining factors influencing e-learning engagement among university students during covid-19 pandemic: a mediating role of “learning persistence”. Interact Learn Environ. 2022:1-28.

  • 3.

    Belcher DD. On becoming facilitators of multimodal composing and digital design. J Second Lang Writ. 2017;38:80-5.

  • 4.

    Casanave CP. Controversies in Second Language Writing: Dilemmas and decisions in research and instruction. 2nd ed. Michigan, USA: the University of Michigan Press; 2013.

  • 5.

    Hafner C. Genre innovation and multimodal expression in scholarly communication. Ibérica. 2018;(36):15-42.

  • 6.

    Mehlenbacher AR. Crowdfunding Science: Exigencies and Strategies in an Emerging Genre of Science Communication. Tech Commun Q. 2017;26(2):127-44.

  • 7.

    Hafner C, Chik A, Jones R. Digital literacies and language learning. Lang Learn Technol. 2015;19(3):1-7.

  • 8.

    Kress G. Multimodality: Challenges to Thinking about Language. TESOL Quarterly. 2000;34(2).

  • 9.

    Yi Y, Shin D, Cimasko T. Multimodal Literacies in Teaching and Learning English In and Outside of School. In: de Oliveira LC, editor. The Handbook of TESOL in K‐12. Haboken, USA: Wiley; 2019. p. 163-77.

  • 10.

    Eccles JS. Studying the development of learning and task motivation. Learn Instr. 2005;15(2):161-71.

  • 11.

    Kumari A, Chamundeswari S. Self-Concept and Academic Achievement of Students at the Higher Secondary Level. Journal of Sociological Research. 2013;4(2).

  • 12.

    Campbell JD, Lavallee LF. Who am I? The Role of Self-Concept Confusion in Understanding the Behavior of People with Low Self-Esteem. In: Baumeister RF, editor. Self-Esteem: The Puzzle of Low Self-Regard. New York, USA: Springer; 1993. p. 3-20.

  • 13.

    Damon W, Hart D. The Development of Self-Understanding from Infancy Through Adolescence. Child Dev. 1982;53(4).

  • 14.

    Simons J, Capio CM, Adriaenssens P, Delbroek H, Vandenbussche I. Self-concept and physical self-concept in psychiatric children and adolescents. Res Dev Disabil. 2012;33(3):874-81. [PubMed ID: 22245732].

  • 15.

    Arens AK, Jansen M. Self-concepts in reading, writing, listening, and speaking: A multidimensional and hierarchical structure and its generalizability across native and foreign languages. J Educ Psychol. 2016;108(5):646-64.

  • 16.

    Guo J, Nagengast B, Marsh HW, Kelava A, Gaspard H, Brandt H, et al. Probing the Unique Contributions of Self-Concept, Task Values, and Their Interactions Using Multiple Value Facets and Multiple Academic Outcomes. AERA Open. 2016;2(1).

  • 17.

    Huang C. Self-concept and academic achievement: a meta-analysis of longitudinal relations. J Sch Psychol. 2011;49(5):505-28. [PubMed ID: 21930007].

  • 18.

    Marsh HW, Pekrun R, Lichtenfeld S, Guo J, Arens AK, Murayama K. Breaking the double-edged sword of effort/trying hard: Developmental equilibrium and longitudinal relations among effort, achievement, and academic self-concept. Dev Psychol. 2016;52(8):1273-90. [PubMed ID: 27455188].

  • 19.

    Viljaranta J, Tolvanen A, Aunola K, Nurmi JE. The Developmental Dynamics between Interest, Self-concept of Ability, and Academic Performance. Scand J Educ Res. 2014;58(6):734-56.

  • 20.

    Zimmerman BJ, Risemberg R. Becoming a self-regulated writer: A social cognitive perspective. Contemp Educ Psychol. 1997;22(1):73-101.

  • 21.

    Dörnyei Z. The psychology of the language learner: Individual differences in second language acquisition. 1st ed. Abingdon, England: Routledge; 2014.

  • 22.

    Pintrich PR, Wolters C, Baxter GP. 2. assessing metacognition and self-regulated learning. In: Schraw G, Impara JC, editors. Issues in the Measurement of Metacognition. Lockport, USA: Lincoln; 2000.

  • 23.

    Anderson JR. The adaptive nature of human categorization. Psychol Rev. 1991;98(3):409-29.

  • 24.

    Zhang LJ. Constructivist pedagogy in strategic reading instruction: exploring pathways to learner development in the English as a second language (ESL) classroom. Instr Sci. 2007;36(2):89-116.

  • 25.

    Zhang LJ, Gu PY, Hu G. A cognitive perspective on Singaporean primary school pupils' use of reading strategies in learning to read in English. Br J Educ Psychol. 2008;78(Pt 2):245-71. [PubMed ID: 17588294].

  • 26.

    Dhawan S. Online Learning: A Panacea in the Time of COVID-19 Crisis. J Educ Technol Syst. 2020;49(1):5-22.

  • 27.

    Yan H. Data-Driven Smart e-Learning for English for Specific Purposes. In: Uskov VL, Howlett RJ, Jain LC, editors. Smart Education and e-Learning - Smart Pedagogy. New York City, USA: Springer; 2022. p. 151-9.

  • 28.

    Heidari-Shahreza MA, Najafi M, Ketabi S. The effect of adobe connect virtual classrooms on medical students' technical vocabulary learning: Achievements and challenges. Int Arch Health Sci. 2021;8(3).

  • 29.

    Liu W, Wang C. Academic self-concept: A cross-sectional study of grade and gender differences in a Singapore secondary school. Asia Pac Educ Rev. 2005;6(1):20-7.

  • 30.

    Minchekar VS. Academic Self Concept Scale for Adolescents: Development, Reliability and Validity of ASCS. International Journal of Research and Analytical Reviews. 2019;6(1):26-30.

  • 31.

    Brown J, Miller W, Lawendowski L, Vandecreek L, Jackson T. The Self-Regulation Questionnaire. In: Vandecreek L, Jackson T, editors. Innovations in clinical practice: A source book. 17. Sarasota, USA: Professional Resource Press/Professional Resource Exchange; 1999.

  • 32.

    Neal DJ, Carey KB. A follow-up psychometric analysis of the self-regulation questionnaire. Psychol Addict Behav. 2005;19(4):414-22. [PubMed ID: 16366813]. [PubMed Central ID: PMC2431129].

  • 33.

    Carey KB, Neal DJ, Collins SE. A psychometric analysis of the self-regulation questionnaire. Addict Behav. 2004;29(2):253-60. [PubMed ID: 14732414].

  • 34.

    Gavora P, Jakešová J, Kalenda J. The Czech Validation of the Self-regulation Questionnaire. Procedia Soc Behav Sci. 2015;171:222-30.

  • 35.

    Gao X. Changes in Chinese Students’ Learner Strategy Use after Arrival in the UK: a Qualitative Inquiry. In: Palfreyman D, Smith RC, editors. Learner Autonomy across Cultures. London, England: Palgrave Macmillan; 2003. p. 41-57.

  • 36.

    Graesser AC, Lu S, Jackson GT, Mitchell HH, Ventura M, Olney A, et al. AutoTutor: a tutor with dialogue in natural language. Behav Res Methods Instrum Comput. 2004;36(2):180-92. [PubMed ID: 15354683].

  • 37.

    Seker M. The use of self-regulation strategies by foreign language learners and its role in language achievement. Lang Teach Res. 2016;20(5):600-18.

  • 38.

    Zimmerman BJ, Schunk DH. Self-regulated learning and academic achievement: Theoretical perspectives. Abingdon, England: Routledge; 2001.

  • 39.

    Azevedo R. Theoretical, conceptual, methodological, and instructional issues in research on metacognition and self-regulated learning: A discussion. Metacogn Learn. 2009;4(1):87-95.

  • 40.

    Bernacki ML, Aguilar AC, Byrnes JP. Self-Regulated Learning and Technology-Enhanced Learning Environments. In: Dettori G, Persico D, editors. Fostering Self-Regulated Learning through ICT. Hershey, USA: IGI Global; 2011. p. 1-26.

  • 41.

    Orhan MA, Castellano S, Khelladi I, Marinelli L, Monge F. Technology distraction at work. Impacts on self-regulation and work engagement. J Bus Res. 2021;126:341-9.

  • 42.

    Hodges C, Forrest Cowan S. Preservice Teachers’ Views of Instructor Presence in Online Courses. J Digit Learn Teach Educ. 2012;28(4):139-45.

  • 43.

    Kassab SE, Al-Shafei AI, Salem AH, Otoom S. Relationships between the quality of blended learning experience, self-regulated learning, and academic achievement of medical students: a path analysis. Adv Med Educ Pract. 2015;6:27-34. [PubMed ID: 25610011]. [PubMed Central ID: PMC4293215].

  • 44.

    Barnard L, Lan WY, To YM, Paton VO, Lai S. Measuring self-regulation in online and blended learning environments. Internet High Educ. 2009;12(1):1-6.

  • 45.

    Alexander S. The relationship of self-concept, IQ, academic performance, and stressors to coping abilities for urban African-American gifted students [dissertation]. Cleveland, USA: Cleveland State University; 1997.

  • 46.

    Zhan Z, Mei H. Academic self-concept and social presence in face-to-face and online learning: Perceptions and effects on students' learning achievement and satisfaction across environments. Comput Educ. 2013;69:131-8.

  • 47.

    Lim DH. Perceived differences between classroom and distance education: Seeking instructional strategies for learning applications. Int J Educ Technol. 2002;3(1).

  • 48.

    Relan A, Gillani BB. Web-based instruction and the traditional classroom: Similarities and differences. Web-based instruction. 1997;62:41-6.