The Influence of Learning Style Preferences on Student Success in Online Versus Face-to-Face Environments

Aragon, Steven R.; Johnson, Scott D.; Shaik, Najmuddin

doi:10.1207/s15389286ajde1604_3

Cited by 173 publications

(138 citation statements)

References 7 publications

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“…No other student characteristics significantly correlated with performance. These findings are consistent with previous research results on prior knowledge (McDonald and Stevenson, 1998;Stiller, 2003Stiller, , 2009Amadieu et al, 2009), intrinsic motivation (Fredericksen et al, 2000;Ali and Franklin, 2001;Aragon et al, 2001;Sankaran and Bui, 2001;Delialioglu, 2005;Waschull, 2005;Yukselturk and Bulut, 2007;Artino, 2008), computer attitude (Stiller, 2009, in press), and computer anxiety (Desai, 2001;Sam et al, 2005;Saadé and Kira, 2009;Hauser et al, 2012). The analyses of attitude toward computers and computer anxiety produced equivalent results, which reflects the redundancy of the measures as shown by the very high correlation between them.…”

Section: Discussing Theory and Previous Researchsupporting

confidence: 93%

“…Analogously, intrinsic motivation is also assumed to play a key part in successful distance and online learning (Jones and Issroff, 2007). In general, positive correlations between motivation and performance have been reported (Fredericksen et al, 2000;Ali and Franklin, 2001;Aragon et al, 2001;Sankaran and Bui, 2001;Delialioglu, 2005;Waschull, 2005;Yukselturk and Bulut, 2007;Artino, 2008). A higher level of intrinsic motivation might make learners invest more resources in learning and process information more deeply, thus contributing to successfully passing tests (van Merriënboer and Ayres, 2005;Moreno, 2006;Moreno and Mayer, 2007;Orvis et al, 2008).…”

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confidence: 99%

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Cognitive Loads in a Distance Training for Trainee Teachers

Stiller

Bachmaier

2018

Front. Educ.

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One of the persistent problems of online and distance learning is how to effectively support students' successful learning outcomes. A promising approach to this issue is to investigate the extent that learning is determined by learner characteristics and skills. This study explores the effects of domain-specific prior knowledge, intrinsic motivation, computer attitude, computer anxiety, and learning management skills on learning in a distance-training course on media pedagogy for teachers. The data were collected from 127 trainee teachers who responded to various questionnaires and completed knowledge tests. We investigated whether the selected characteristics of learners could be used to model five cognitive load assessments that included ratings of content difficulty, learning ease, experienced pressure/tension, effort/importance while learning, and performance in tests. Computer anxiety and computer attitude were significant predictors of content difficulty, learning ease, and experienced pressure/tension. In addition, higher learning-strategy skills reduced ratings of content difficulty. Effort/Importance ratings were best modeled by intrinsic motivation and learning strategy usage. Performance was best predicted by prior knowledge, intrinsic motivation, and computer attitude.Keywords: distance training, teacher training, higher education, self-regulated learning, computer attitude, computer anxiety, cognitive load theory INTRODUCTIONDistance and online learning have become common and reputable educational methods in vocational training and higher education (e.g., Allen and Seaman, 2016). Numerous studies investigating theoretical and applied aspects of distance and online learning have been published. However, the problem of effectively supporting successful student learning still persists (Jo et al., 2016). To gain insight into this problem, one line of research has focused on the characteristics and skills of distance and online learners and the extent to which they predict learning processes and outcomes. To advance this research, we investigated the extent to which a selected set of learner characteristics affect cognitive load of trainee teachers while learning and performing in a script-based distance-training course about media pedagogy. As characteristics, domain-specific prior knowledge, intrinsic motivation, computer attitude, computer anxiety, and learning strategies were selected, because all of them have shown to be relevant for distance and online learning in empirical studies, and they represent cognitive, motivational, affective, and skill aspects. As theoretical background of the current study, the cognitive load theory (CLT) was chosen (Sweller, 2010). Stiller and Bachmaier Cognitive Loads in Distance TrainingThe CLT highlights the role of cognitive capacity in working memory for successful learning outcomes (Sweller, 2010). Working memory is limited in capacity. If memory items cannot be rehearsed, they decline within seconds. If the material to be learned is complex, that is, it compris...

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Section: Discussing Theory and Previous Researchsupporting

confidence: 93%

mentioning

confidence: 99%

Cognitive Loads in a Distance Training for Trainee Teachers

Stiller

Bachmaier

2018

Front. Educ.

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“…Although collaborative style was not generally indicative of overall exam scores in our study, two-way ANOVA yielded a relatively weak association between collaborative style and success, especially in female students. Our results are in line with previous data revealing that college students with high collaborative styles were more likely to receive A's in technology-based and traditional courses [22]. However, not all studies support this association [9].…”

Section: Journal Of Clinical and Analytical Medicinesupporting

confidence: 92%

Learning Styles, Academic Achievement, and Gender in a Medical School Setting

Kulaç¹,

Sezi̇k²,

Aşçı³

et al. 2015

Ann Clin Anal Med

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ÖzetAmaç: Çalışmada klinik öncesi dönemdeki tıp öğrencilerinin öğrenme stilleri, cinsiyet ve akademik başarıları arasındaki ilişkinin değerlendirilmesi amaç-lanmıştır. Gereç ve Yöntem: Fakültemizdeki tüm klinik öncesi dönem tıp öğ-rencileri çalışmaya alınmıştır. Öğrenme stillerini belirlemek için öğrenme stillerini altı kategoride (bağımsız, bağımlı, rekabetçi, işbirlikçi, çekingen ve katılımcı) inceleyen Grasha -Reichmann Öğrenme Stilleri Ölçeği kullanılmış-tır. Öğrencilerin akademik başarıları kurul sınav puanları, final sınavı puanı ve geçme notu ile değerlendirilmiştir. Bulgular: Tıp öğrencilerinin en sık rekabetçi (% 34.8) ve işbirlikçi (% 33.7) öğrenme stillerine sahip olduğu saptanmıştır. Rekabetçi öğrenme stili ile final puanı ve geçme notu arasında anlamlı ilişki görülmüştür. Ayrıca rekabetçi ve işbirlikçi kız öğrenciler diğerleri-ne göre anlamlı olarak daha yüksek puan almışlardır. Tartışma: Fakültemiz-de rekabetçi öğrenciler diğerlerine göre daha yüksek başarı puanları elde etmişlerdir. Kız rekabetçi ve işbirlikçi öğrencilerin rekabetçi ve işbirlikçi erkeklerden daha yüksek puan almaları ile sözkonusu ilişkide cinsiyetin de etkili olduğu görülmüştür. Anahtar KelimelerEğitimsel Başarı; Cinsiyet; Öğrenme Stili; Tıp Eğitimi Abstract Aim: To investigate correlations among learning styles, academic achievement and gender of medical students in preclinical years. Material and Method: All medical students in preclinical years at our institution were approached. The Grasha-Reichmann Student Learning Styles Scale was used to define primary learning style of the participants in six categories (independent, dependent, competitive, collaborative, avoidant, and participant). Academic achievement criteria included thematic block exam scores, final exam scores, and passing grades. Results: Competitive (34.8%) and collaborative (33.7%) were the most frequent learning styles among participants. Competitive learning style was associated with higher mean final exam scores and passing grades. Female students with competitive and collaborative learning styles scored significantly higher than male students. Discussion: Students with competitive learning styles had higher academic achievement than others. Among students with competitive and collaborative styles, there was a gender difference in favor of female students in terms of academic achievement.

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“…On the other side, several studies have classified various challenges of online learning, including: a) low retention rates (Ali & Leeds, 2009;Angelina, Williams, & Natvig, 2007;Holder, 2007;Lee & Choi, 2011), b) a greater responsibility for study and requirements of additional skills or competencies (Aragon, Johnson, & Shaik, 2002;Zawacki-Richter, 2004), and c) an absence of the sense of social belonging (Ma & Yuen, 2010). According to Karber (2003), learners tend to spend more time studying when they take an online course than they might with a traditional face-to-face course.…”

Section: Benefits and Challenges Of Online Learningmentioning

confidence: 99%

An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument

Richardson

2015

OLJ

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The purpose of this study was to develop an effective instrument to measure student readiness in online learning with reliable predictors of online learning success factors such as learning outcomes and learner satisfaction. The validity and reliability of the Student Online Learning Readiness (SOLR) instrument were tested using exploratory factor analysis (EFA) and reliability analysis. Twenty items from three competencies–social competencies, communication competencies, and technical competencies–were designated for the initial instrument based on the Student Online Learning Readiness (SOLR) Model as a new conceptual model. An exploratory factor analysis (EFA) revealed that four factor-structures of the instrument of student readiness in online learning explained 66.69% of the variance in the pattern of relationships among the items. All four factors had high reliabilities (all at or above Cronbach’s Î± > .823). Twenty items remained in the final questionnaire after deleting two items which cross-loaded on multiple factors (social competencies with classmates: five items; social competencies with instructor: five items; communication competencies: four items; and technical competencies: six items). The four-factor structure of the Student Online Learning Readiness (SOLR) instrument has been confirmed through this study. Educators can use the Student Online Learning Readiness (SOLR) instrument in order to gain a better understanding of the level of freshmen college students’ online learning readiness by measuring their social, communication, and technical competencies. In addition, this study examined two factors of social integration in Tinto’s Student Integration Model (SIM) and has introduced the Student Online Learning Readiness (SOLR) conceptual model with the purpose of extending Tinto’s social integration model to online learning environments.

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The Influence of Learning Style Preferences on Student Success in Online Versus Face-to-Face Environments

Cited by 173 publications

References 7 publications

Cognitive Loads in a Distance Training for Trainee Teachers

Cognitive Loads in a Distance Training for Trainee Teachers

Learning Styles, Academic Achievement, and Gender in a Medical School Setting

An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument

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