Introduction
Review methodology
Review protocol
Inclusion and exclusion criteria
Inclusion | Exclusion |
---|---|
Complete and full-length studies | Incomplete and full length is not available to download |
Published between January 2014 and March 2019 | Published beyond this period |
Published in the English language | Published other than English |
Relevant and searched words present in title, abstracts or in the keywords section | Not relevant and searched words are not present in the title, abstracts or in the keywords section |
Search strategy process
Selection process
Databases | Before results | After results |
---|---|---|
IEEE Xplore | 158 | 8 |
ScienceDirect | 90 | 14 |
Emerald insight | 81 | 4 |
AIS Electronic Library | 52 | 1 |
Sage | 50 | 5 |
ACM Digital Library | 73 | 5 |
Springer Link | 38 | 1 |
Taylor and Francis | 17 | 2 |
Google Scholar | 1 | – |
40 |
Quality assessment
- QA1. Does the topic address in the study related to big data in education?
- QA2. Does the study describe the context?
- QA3. Does the research method given in the paper?
- QA4. Does data collection portray in the article?
Data extraction and synthesis
Data Extraction Items | Narration |
---|---|
Research ID | To give a distinctive number to research paper |
Author Names | The creator of the study |
Study Title | The name of the study |
Publication Year | The year of publication (e.g. 2019) |
Publication Place | E.g. Journal, Conference, and workshop, etc. |
Research Theme | The particular domain/area of study (e.g. e-learning, student grading systems, etc) |
Research Context | The theoretical background, perspective or framework |
Research Method | Quantitative, qualitative and mix method, etc. |
Data Collection Method | Survey, interviews and observations, etc. |
Findings
What are the trends in the papers published on big data in education?
Publications | Sources |
---|---|
9 | Science Direct journals |
5 | SAGE Journals |
1 | SpringerLink Journals |
6 | IEEE conferences |
5 | ACM conferences |
1 | IEEE Symposium |
4 | Emraldinsight journals |
5 | Science Direct Conferences |
1 | IEEE Workshop |
1 | AISeL Conference |
2 | Taylor and Francis Journals |
Temporal view of researches
Citation
R-ID | Title | Citation |
---|---|---|
R4 | The role of big data and cognitive computing in the learning process | 23 |
R8 | Using Big Data in the Academic Environment | 22 |
R32 | Internet use that reproduces educational inequalities: Evidence from big data | 15 |
R40 | Big Data Application in Education: Dropout Prediction in Edx MOOCs | 28 |
R36 | Discovering Big Data Modeling for Educational World | 25 |
R21 | The Life Between Big Data Log Events | 23 |
R33 | Mining theory-based patterns from Big data: Identifying self regulated learning strategies in Massive Open Online Courses | 53 |
R23 | CS principles goes to middle school | 29 |
R5 | Toward an integration of Big Data, technology and information systems competencies into the accounting curriculum | 54 |
R27 | Education and training for a successful career in Big Data and Business Analytics | 51 |
R38 | Data entry: towards the critical study of digital data and education | 168 |
Research methodologies
Data collection methods
What research themes have been addressed in educational studies of big data?
Studies Themes | Description | References |
---|---|---|
Learners behavior and performance | Studies that investigated the learner’s attitude, satisfaction, strategies, behavior, big data frameworks, adaptive learning, teaching and learning analytics. | (Cantabella, Martínez-España, Ayuso, Yáñez, & Muñoz, 2019; Chaurasia & Frieda Rosin, 2017; Chaurasia, Kodwani, Lachhwani, & Ketkar, 2018; Coccoli, Maresca, & Stanganelli, 2017; Dessì, Fenu, Marras, & Reforgiato Recupero, 2019; Dubey & Gunasekaran, 2015; Elia, Solazzo, Lorenzo, & Passiante, 2018; Hirashima, Supianto, & Hayashi, 2017; Lia & Zhaia, 2018; Liang, Yang, Wu, Li, & Zheng, 2016; Maldonado-Mahauad, Pérez-Sanagustín, Kizilcec, Morales, & Munoz-Gama, 2018; Muthukrishnan & Yasin, 2018; Nie et al., 2018; Oi et al., 2017; Roy & Singh, 2017; Sedkaoui & Khelfaoui, 2019; Shorfuzzaman et al., 2019; Su, Ding, Lue, Lai, & Su, 2017; Troisi, Grimaldi, Loia, & Maione, 2018; Veletsianos, Reich, & Pasquini, 2016; Yang & Du, 2016) |
Modeling and educational Data Warehouse | Studies that introduced big data modeling and analyzed big data tools (Hadoop) with data warehouses. It explored the cloud environment and cluster analysis for accessibility and processing of educational data. | |
Improvement of the educational system | Studies that analyzed statistical tools, measurements, challenges, and ICT effectiveness. It emphasized on training and various implications. It introduced a ranking system and observes the usage of websites to improve the educational system. | |
Integration of big data into the curriculum | Studies that introduce big data topics into different courses and highlighted its implications for education. |
Learners behavior and performance | Number of Studies |
---|---|
Teaching and learning analytics | 8 |
Big data framework | 3 |
User behavior and attitude | 6 |
Learner strategies | 2 |
Adaptive learning | 1 |
Learners satisfaction | 1 |
Modeling and educational data warehouses | Number of Studies |
---|---|
Cloud Environment | 1 |
Big data Modeling | 3 |
Cluster Analysis | 1 |
Data warehouse | 1 |
Improvement of the educational system | Number of Studies |
---|---|
Statistical tools and measurements | 2 |
Educational research Implications | 1 |
Ranking system | 1 |
Usage of websites | 1 |
Big data training | 1 |
Big data effectiveness and challenges | 3 |