Introduction to Big Data in Education and Its Contribution to the Quality Improvement Processes

Vaitsis, Christos; Hervatis, Vasilis; Zary, Nabil

doi:10.5772/63896

Cited by 27 publications

(25 citation statements)

References 23 publications

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“…The rapid generation of data by different devices and people has reached huge sizes exceeding the capacity of hardware or human abilities to process and manipulate (Vaitsis, Hervatis, & Zary, ). Subsequently, there is a general tendency in the literature to conceptualize Big Data along the magnitude of data.…”

Section: Reconceptualization Of Big Data In Educationmentioning

confidence: 99%

Big Data and data science: A critical review of issues for educational research

Daniel

2017

Brit J Educational Tech

168

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Big Data refers to large and disparate volumes of data generated by people, applications and machines. It is gaining increasing attention from a variety of domains, including education. What are the challenges of engaging with Big Data research in education? This paper identifies a wide range of critical issues that researchers need to consider when working with Big Data in education. The issues identified include diversity in the conception and meaning of Big Data in education, ontological, epistemological disparity, technical challenges, ethics and privacy, digital divide and digital dividend, lack of expertise and academic development opportunities to prepare educational researchers to leverage opportunities afforded by Big Data. The goal of this paper is to raise awareness on these issues and initiate a dialogue. The paper was inspired partly by insights drawn from the literature but mostly informed by experience researching into Big Data in education.As a new research paradigm, Big Data in education stimulates new ways of framing research questions, designing studies, analyzing and visualizing data (Daniel, 2015;Dede et al., 2016). With the availability of large amounts of data in education, researchers can investigate subgroups within a population (a particular group of people), without necessarily relying on sophisticated probabilistic methods (Mayer-Sch€ onberger & Cukier, 2013). Further, Big Data tools enable researchers to collect large amounts of research data with relatively low cost (Mayer-Sch€ onberger, 2015).

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Section: Reconceptualization Of Big Data In Educationmentioning

confidence: 99%

Big Data and data science: A critical review of issues for educational research

Daniel

2017

Brit J Educational Tech

168

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“…These skills contribute to the representation of the human body, allowing the understanding of relation between structures and three-dimensionality (Guillot et al, 2007;Langlois et al, 2015;Langlois et al, 2017). Moreover, the relevance of spatial abilities in anatomy education has shown an impact in different medical fields such as medical imaging (van der Gijp et al, 2014) or laparoscopic surgery (Roch et al, 2018).…”

Section: Spatial Abilities In the Context Of Anatomy Educationmentioning

confidence: 99%

“…Indeed, spatial abilities serve as a predictor of the learning time, performance and knowledge acquisition in anatomy (Guillot et al, 2007;Berney et al, 2015;Langlois et al, 2017). Likewise, several studies drew strong correlations between these skills and the easiness to incorporate new information or techniques in the surgical field (Wanzel et al, 2003;Stefanidis et al, 2006;Buckley et al, 2014;Langlois et al, 2015), namely laparoscopic surgery (Stefanidis et al, 2006;Kramp et al, 2016;Roch et al, 2018), and radiology (van der Gijp et al, 2014;Birchall, 2015).…”

Section: Students' Spatial Abilities Performance Improvementmentioning

confidence: 99%

“…In addition, CAL and CBA contribute to a better understanding of their users since information about them and their interactions or learning patterns is gathered (Vaitsis et al, ). This approach is based on Learning Analytics' concepts, and advocates the measurement, collection, analysis, and reporting of data about learners and their contexts, to understand and optimize the learning process so as to enhance/personalize the learning experience (Siemens, ; Pinnell et al, ).…”

Section: Introductionmentioning

confidence: 99%

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The Role of Anatomy Computer‐Assisted Learning on Spatial Abilities of Medical Students

Guimarães

Firmino-Machado

Tsisar

et al. 2018

Anatomical Sciences Ed

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Currently, medical education context poses different challenges to anatomy, contributing to the introduction of new pedagogical approaches, such as computer-assisted learning (CAL). This approach provides insight into students' learning profiles and skills that enhance anatomy knowledge acquisition. To understand the influence of anatomy CAL on spatial abilities, a study was conducted. A total of 671 medical students attending Musculoskeletal (MA) and Cardiovascular Anatomy (CA) courses, were allocated to one of three groups (MA Group, CA Group, MA + CA Group). Students' pre-training and post-training spatial abilities were assessed through Mental Rotations Test (MRT), with scores ranging between 0-24. After CAL training sessions, students' spatial abilities performance improved (9.72 ± 4.79 vs. 17.05 ± 4.57, P < 0.001). Although male students in both MA Group and CA Group show better baseline spatial abilities, no sex differences were found after CAL training. The improvement in spatial abilities score between sessions (Delta MRT) was correlated with Musculoskeletal Anatomy training sessions in MA Group (r = 0.333, P < 0.001) and MA + CA Group (r = 0.342, P < 0.001), and with Cardiovascular Anatomy training sessions in CA Group (r = 0.461, P = 0.001) and MA + CA Group (r = 0.324, P = 0.001). Multiple linear regression models were used, considering the Delta MRT as dependent variable. An association of Delta MRT to the amount of CAL training and the baseline spatial abilities was observed. The results suggest that CAL training in anatomy has positive dose-dependent effect on spatial abilities. Anat Sci Educ 00: 000-000. © 2018 American Association of Anatomists.

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“…Mesolevel -It describes many courses in an academic year. Macrolevel -It describes several programs in the institution [8]. The analytics in the higher education sector can be of two types.…”

Section: Analytics In Higher Educationmentioning

confidence: 99%

A Review of Big Data Analytics in Sector of Higher Education

Vatsala¹,

Jadhav²,

Sathyaraj³

2017

IJERA

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This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are-Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.

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Introduction to Big Data in Education and Its Contribution to the Quality Improvement Processes

Cited by 27 publications

References 23 publications

Big Data and data science: A critical review of issues for educational research

Big Data and data science: A critical review of issues for educational research

The Role of Anatomy Computer‐Assisted Learning on Spatial Abilities of Medical Students

A Review of Big Data Analytics in Sector of Higher Education

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