Academic Social Networks and Learning Analytics to Explore Self-Regulated Learning: a Case Study

Barujel, Adriana Gewerc; Groba, Ana Rodríguez; Piñeiro, Esther Martínez

doi:10.1109/rita.2016.2589483

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…These commonalities were grouped into the following headings: 1) student interaction with the community, the 2) interaction vs student grades, the 3) student experience. Cheng et al [10] Gewerc et al [11] Lee et al [12] Morillas et al [13] Warin et al [14] Cruz-Bentio et al [15] Encalada et al [16] Rambocas et al [17] Rodriguez et al [18] Rajab K.D. [19] Motivation 1 1…”

Section: Resultsmentioning

confidence: 99%

“…In their study they had an overwhelmingly positive 72.8% relationship of higher interaction levels of student achieved better grades. Gewerc et al may have found limited evidence of a positive relationship but concluded that the students with the lowest levels of participation often corresponded to the students with the lowest marks [11]. In contrast, Cheng et al found that while supplementary benefits were seen in the experimental group, there was no difference in the grades achieved [10].…”

Section: B Interaction Vs Gradesmentioning

confidence: 98%

“…Personal student issues also come into play when interacting with in a new community. Weaker communication skills showed an increase in the difficulty to immerse themselves in some communities [11]. This could be the result of a reported lower level of content dissemination and guidance from lectures in digital communities.…”

Section: A Student Interaction With the Communitiymentioning

confidence: 99%

See 2 more Smart Citations

Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry

Jackson

Nikolic

Shen

et al. 2018

2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)

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Digital learning communities have become a focal point of organizational development. The education industry has begun to follow suit by using the same technologies to enhance the learning process through a deeper process of participation. These technological tools complement sound learning design to bring a wealth of benefits to students. These benefits are not without peril. New technological tools shift common issues of education into online environments. This article reviews recent implementations of digital communities and highlights their influencing factors. The factors are then connected to existing factors in knowledge management literature. The key factors found are A) Student interaction with the community, B) Interaction vs grades and C) Student experiences.

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Section: Resultsmentioning

confidence: 99%

Section: B Interaction Vs Gradesmentioning

confidence: 98%

Section: A Student Interaction With the Communitiymentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry

Jackson

Nikolic

Shen

et al. 2018

2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)

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“…Çevrimiçi öğrenme ortamları, Web ve internet teknolojilerinin ilerlemesiyle çok hızlı şekilde yaygınlaşmıştır (Gewerc, Rodríguez Groba & Martínez Piñeiro, 2016). Çevrimiçi öğrenmede kişilerin kendi öğrenmelerinin sorumluluğunu alma gereksinimleri, ÖDÖ'ye dayalı öğrenmeye duyulan ihtiyacı arttırmıştır (Üredi & Üredi, 2007).…”

Section: Araştırma Sorunsalıunclassified

Öğrenme analitikleri ile öz-düzenleyici öğrenme arasındaki ilişkinin sistematik alanyazın taramasıyla incelenmesi

Topallı

Fırat

2023

Açıköğretim Uygulamaları Ve Araştırmaları Dergisi

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Öğrenme deneyimlerinin dijitale geçmesiyle birlikte, öğrenenlerin bu ortamlarda gerçekleştirdikleri tüm etkileşimler izlenebilir hale gelmiştir. Bu da öğrenme analitiklerini hızla yaygınlaşmasına yol açmıştır. Öğrenme analitikleri, öğrenmenin kendisini ve öğrenme çevrelerini anlamak ve iyileştirmek için kullanılır. Öğrenenlerle ve öğrenme bağlamlarıyla ilgili verilerin toplaması, ölçülmesi, analiz edilmesi, görselleştirilmesi ve en önemlisi de analitik sonuçların öğrenme çevrelerine yansıtılması süreçleriyle ilgilenir. Öğrenme analitikleri, bireyin kendini izlemesine ve kendi performansını değerlendirmesine olanak sağladığı için öğrenenlerin öz düzenleyici öğrenmelerine de etki etmektedir. Alanyazında öğrenme analitikleri ile öz düzenleyici öğrenme ilişkisini inceleyen çalışmalar yer almaktadır. Bu araştırmada öğrenme analitiklerinin öz düzenleyici öğrenmeye etkilerini inceleyen çalışmalara yönelik bir sistematik alanyazın taraması yapılmıştır. Bu amaçla Web of Secience veri tabanı kullanılarak başlığında “learning analytic” ve “self-regulated” anahtar kelimeleri bulunan makaleler incelenmiştir. Çalışma sonucunda öğrenme analitikleri kullanmanın, öz düzenleyici öğrenme becerilerinin tespitinde ve geliştirilmesine yardımcı olduğu sonucuna ulaşılmıştır.

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“…Research within organizational contexts furthermore indicates that high performing individuals tend to have highly diverse networks [ 20 ]. Research into the relationship between SRL and social networks in virtual learning environments suggests that it is the ‘good’ self-regulators who position themselves in the centre of a network from the very start of engagement in the new environment, creating connections with many others in their network [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study

et al. 2021

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Introduction Recent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students’ interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students’ co-regulatory networks, perceived learning opportunities, and self-regulated learning. Methods The authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students’ perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning. Results Across all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = −0.474, p < 0.001). Students’ perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students’ perceptions of learning opportunities. Discussion The present study reinforces the importance of co-regulatory networks for medical students’ self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students’ self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.

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Academic Social Networks and Learning Analytics to Explore Self-Regulated Learning: a Case Study

Cited by 8 publications

References 18 publications

Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry

Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry

Öğrenme analitikleri ile öz-düzenleyici öğrenme arasındaki ilişkinin sistematik alanyazın taramasıyla incelenmesi

Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study

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Academic Social Networks and Learning Analytics to Explore Self-Regulated Learning: a Case Study

Cited by 8 publications

References 18 publications

Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry

Knowledge Sharing in Digital Learning Communities: A Comparative Review of Issues between Education and Industry

Öğrenme analitikleri ile öz-düzenleyici öğrenme arasındaki ilişkinin sistematik alanyazın taramasıyla incelenmesi

Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a&nbsp;social network study

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Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study