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Innovative Teaching in Higher Education: The Big Data Approach
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DOI: 10.13140/RG.2.2.10543.53924
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Innovative Teaching In Higher Education: The Big Data Approach 
 
 
 
Miftachul HUDA 
Sultan Omar ‘Ali Saifuddien Centre of Islamic Studies, Universiti Brunei Darussalam, Brunei Darussalam 
halimelhuda@gmail.com 
 
Muhammad ANSHARI 
Continuing Education Centre, Universiti Brunei Darussalam, Brunei Darussalam 
anshari.ali@ubd.edu.bn 
 
Mohammad Nabil ALMUNAWAR 
UBD School of Business and Economics, Universiti Brunei Darussalam, Brunei Darussalam 
nabil.almunawar@ubd.edu.bn 
 
Masitah SHAHRILL 
Sultan Hassanal Bolkiah Institute of Education, Universiti Brunei Darussalam, Brunei Darussalam 
masitah.shahrill@ubd.edu.bn 
 
Abby TAN 
Faculty of Science, Universiti Brunei Darussalam, Brunei Darussalam 
abby.tan@ubd.edu.bn 
 
Jainatul Halida JAIDIN 
Sultan Hassanal Bolkiah Institute of Education, Universiti Brunei Darussalam, Brunei Darussalam 
halida.jaidin@ubd.edu.bn 
 
Sabrina DAUD 
UBD School of Business and Economics, Universiti Brunei Darussalam, Brunei Darussalam 
sabrina.daud@ubd.edu.bn 
 
Masairol MASRI 
UBD School of Business and Economics, Universiti Brunei Darussalam, Brunei Darussalam 
masairol.masri@ubd.edu.bn 
 
 
 
ABSTRACT 
Across the Internet where massive amounts of data are being created every second. Widely known as the big 
data, this tool can be signified as the techniques to capture, store and analyse data of resources. It enables us to 
provide instrumental means in formulating data such as for communication, sending informationand online 
activities purpose. Moreover, it can also offer innovative teaching and learning for higher learning institution. 
This study aims to discuss the teaching based on big data application and practices to enhance innovation in 
teaching, learning, sociality, and technology for students. In this study, we deploy thematic analysis to construct 
a model for higher institutions to regulate their scenario on big data application. The findings reveal that it can be 
used to improve decision-makings, provide insights, knowledge discoveries, and optimise learning processes. 
Higher learning institution can adopt big data analytics-based teaching and learning strategy to sustain in 
providing innovative teaching and learning experience to the students with opportunities to improve learning 
experiences to them with big data analytics. In fact, students nowadays rely on online resources and at the same 
time they produce data for sharing purposes. 
Keywords: Big data, Innovative teaching, Pedagogical instrument, Higher education 
 
INTRODUCTION 
Many universities have begun to take benefit of the recent development in Information and Communication 
Technology (ICT) by embracing innovation in online learning (Livingstone, 2012). These higher educational 
institutions are aware that ICT is currently a major component in reforming teaching and learning. The role of 
ICT is to formulate and to enhance mobile teaching and learning process and it drives student-centred learning 
(Williams et al., 2000). For instance, a teacher can manage the delivery of teaching and support materials with 
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Copyright © The Turkish Online Journal of Educational Technology 1210
minimum cost and simultaneously interact with students at any time by using a Learning Management System 
(LMS) (Weaver et al., 2008). The students can also actively participate in online discussion forums moderated 
by the teacher or a tutor. This may bring the innovation in the teaching and learning activities. Besides investing 
in ICT, higher learning institutions have made a lot of investments for teachers in integrating technologies into 
their teaching and learning practices (Palak & Walls, 2009). For instance, introducing multimedia into a 
problem-based learning environment makes students think critically and become active participants in the 
learning process. Furthermore, multimedia-based projects can be used alternatively as an innovative and 
effective tool in a problem-based learning environment for the acquisition of problem-solving skills (Neo & Neo, 
2001). 
 
To be innovative in one’s teaching, we need to take into account the advancement of ICT like smartphones, 
social media, LMS, etc. Smart mobile devices are not limited to just making calls, but students can send text 
messages to anyone around the world (Shin et al., 2011). There are many Apps in smartphones, which make 
sending text messages and calling even easier, and at a lower or no cost at all. Students can even make groups 
using one of the available Apps in their smartphones and send messages instantly to everyone within the group. 
These functions are useful for students, as they can use it to discuss their coursework. Social media is another 
innovative invention used by majority of students (Dabbagh & Kitsantas, 2012). The use of smartphones enables 
students to do their work on their phones which 5 years ago was only possible on computers. Word documents 
can now be accessed and used from the smartphones. If a student is on the go and does not have access to a 
computer or a laptop, the student can just work on a task on his or her phone. These are just some examples 
where ICT has made an impact in learning innovation. Information from multiple sources is needed to enhance it 
and this can be supported easily with the big data framework (Levin & Wadmany, 2006). And especially in 
providing feedback for enhancing innovative teaching performance to enable students to search for resources 
with borderless space (Ertmer, 2005). 
 
Adopted as a new technological approach, the big data framework can incorporate many aspects related to 
massive data generation and its growth in supporting and enhancing innovative teaching and learning. Big data is 
a new generation of data analytical approach designed from collecting, aggregating and analysing very large 
amounts of data (Mayer-Schönberger & Cukier, 2013; Villars et al., 2011). Big data is a utilization of massive 
amounts of data which is created every second across the Internet (Villars et al., 2011) and subsequently, it is 
extracted to gain its potential and value for the user (Mayer-Schönberger & Cukier, 2013). Since teaching and 
learning involve data in every stage, it is interesting to assess big data application in contributing to the 
innovative teaching. In order to deliver innovative teaching, numerous sources provide complex of data like 
social networks, surveys, newspapers, magazines, volume and assortment. The question of what do we want to 
accomplish from these data then arise. This study attempts to propose a model in enhancing innovative teaching 
and learning by incorporating big data framework in a higher education setting. The model is expected to 
contribute by considering multi-channels of sources of knowledge. 
 
INNOVATIVE TEACHING IN HIGHER EDUCATION 
There are many theories and application for innovative teaching related to students’ behaviours, methods, 
approaches, and strategies (Anderson, 2008). Teacher competency plays an important factor in delivering 
innovative teaching in higher education. Those competencies are professional certification, cognitive abilities, 
affective-motivational characteristics, mastery of teaching and learning contents, and pedagogical approach 
(Blömeke & Delaney, 2014; Harris et al., 2009). There are four core competencies to deliver innovative 
teaching; innovative learning competence, innovative social competency, innovative educational competency, 
and innovative technological competency (Zhu et al., 2013). 
 
Innovative learning competence refers to the knowledge on how teachers update subject knowledge and contents 
enhance methods to gather new knowledge, improve ways in getting learning materials, and solve learning 
problems through self-reflection (Soto Gómez et al., 2015). This competence aims to improve individual 
knowledge competencies so that the teacher can deliver knowledge effectively to his or her students. In addition, 
the ability to access reliable data can help the teacher enhance innovative learning (Livingstone, 2012). 
 
Innovative social competence is an ability to communicate socially with students from different backgrounds 
(Jeffrey & Craft, 2004). In online learning, this refers to the innovative skills of the teacher’s ability to tolerate 
the social aspect of the digital nature of communication where students are absent from physical interaction and 
expression. Therefore, innovative social competence must have a presence in order to avoid confusion, 
frustration, miscommunication, and the challenging behaviour of online users. Teachers need innovative social 
competence in online teaching and learning environments (Runco, 2003). 
 
TOJET: The Turkish Online Journal of Educational Technology – November 2016, 
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Innovative educational competency refers to the ability of integrating subject knowledge, pedagogical aspect, 
and learning psychology to achieve the development of students in understanding the topics taught (Runesson & 
Runesson, 2015). In online learning context, the teaching facilitators need to guide students effectively with the 
passion of encouraging active learning in virtual teaching effectively (Asyari et al., 2016). 
 
Innovative technological competency helps teachers to find reliable and comprehensiveinformation from online 
sources to enhance teaching and learning activities (Lawless & Pellegrino, 2007). This also refers to the method 
in data gathering from a multitude of data sources to use effectively in supporting innovative teaching (Salleh, 
2016). In fact, ICT has been used often as a tool to support innovative teaching (McPherson & Nunes, 2004). For 
instance, ICT has been used to develop creative learning for cognitive abilities and emotional aspects of students 
(Anderson, 2008; Bates & Poole, 2003; McPherson & Nunes, 2004; Smith & Hardaker, 2000). According to 
Shin et al. (2011), a teacher uses a smartphone to share online resources directly to students. Utilizing 
smartphones in a class setting combines technological competency and interactive contents’ delivery to promote 
innovative teaching (Bates & Poole, 2003; Salleh, 2016). Additionally, combining technological competency to 
promote innovative teaching can help teachers develop their students’ learning abilities (Bates & Poole, 2003; 
Salleh, 2016). 
 
In this study, we focus on innovative teaching, which is driven by the advancement in ICT such as big data as a 
new concept. The application of data analytics can incorporate many aspects related to massive data generation 
and its growth in supporting and enhancing innovative teaching and learning. These are the benefits that are 
expected to encourage students to enrich their learning experiences as well as to generate value for students’ 
development, performance, and achievement during the learning process. 
 
BIG DATA AND EDUCATION 
With huge amounts of data created every second across the Internet, big data is a new approach in data analytics 
for discovery, analysis, and also to extract value from large volumes of data (Villars et al., 2011). The 
capabilities of big data range from transferring and sharing, predicting, visualizing, capturing and searching data. 
It is known as the fourth generation of computing (Kitchin, 2014). Due to the growth and evolution of ICT, big 
data extends its capacity in terms of volume, velocity, and variety (Anshari et al., 2015). In fact, educational 
institutions produce large amount of data daily, which can be extracted for its value added functions for its 
stakeholders. For instance, big data can the support learning process by providing the access to reliable data 
sources. Furthermore, it can help students’ engagement, interaction, and pervasive knowledge delivery to the 
students and community at large. Big data is in real time with the ability to explore data and understand students’ 
behaviour and is able to offer personalized and customized services to each student. 
 
Moreover, Anshari et al. (2015) stated that the concept of big data provides new opportunities to maximize the 
potential of data collection to gain its value in online learning systems. Through these opportunities, big data 
could offer customization and personalization of knowledge delivery with more precision for each student from 
the perspectives of its stakeholders. For example, students would undergo online learning from the materials 
provided by the instructor. Subsequently, for each topic of discussion the system would supply relevant and 
reliable links to resources (Hoi et al., 2012). Students may generate records of their lives by frequently posting 
details of activities they undertake to understand various students’ blogs so that the instructor understands the 
students’ quality to develop the course structure or make decisions based on this context (Lyon, 2014). 
 
 
Figure 1: Big data analytic process 
 
Figure 1 shows a general concept on how big data generates value for education. Big data analytics is the process 
of extracting raw data either from structured or unstructured data sources. Structured data sources in education 
can come from students’ records, students’ financial records, e-library records, web click behaviours, enterprise 
resource planning (ERP) records, etc. Whereas, unstructured data sources can originate from CCTV, audio, 
social networks interactions, etc. The outcome of the data analytic is the prediction and pattern of each student. 
TOJET: The Turkish Online Journal of Educational Technology – November 2016, 
Special Issue for INTE 2016
Copyright © The Turkish Online Journal of Educational Technology 1212
Prediction and pattern for each student is important in delivering better service for students because, it offers 
personalization of service, customization of modules, and intervention to ensure performance and quality 
control. Customization refers to the ability of students to choose and pick any module or topic that fits their 
interests. Meanwhile, intervention is needed when the performance of students is declining. For example, 
notification or alert can be sent to students when the student cannot perform well in a specific course or topic. 
 
PHASES OF ANALYSES 
This paper focuses on the phases of literature analysis to the reference model. We build on recent reviews of big 
data and innovative teaching in higher education settings. The Google Scholar was used to search keywords on 
innovative teaching, big data and online learning. After removing duplicates and articles beyond the scope of this 
study, 40 peer-reviewed journals and books were then selected for the subsequent review. We employed meta-
synthesis to integrate, evaluate and interpret the findings of multiple research studies so that phenomenological 
and grounded theories can be integrated and used. The ideas were extracted to identify their common features, 
elements, and functionalities. We then analyse and synthesize key elements into new interpretations, 
conceptualizations, and modelling of innovative teaching with big data approach. 
 
DISCUSSION 
Big data is a large amount of data, which capture, link, collect, store and organize data into meaningful 
information. Many organizations stressed the needs to apply big data in their operations. Merely collecting and 
storing data only in educational institutions are not useful without proper analysis. Through proper analysis, one 
can reap the benefits of big data. 
 
However, how is value being created from big data? Big data can capture patterns, trends, and students’ 
behaviour and habits. Big data are gathered from structured data sources and unstructured data sources. 
Structured data sources can be from social networks and Internet messenger activities. While, unstructured 
sources can be gathered from CCTV at the library or even, vehicle registration numbers. From those data 
sources, big data analytic works to form patterns, trends or even forecasting. The analytic results can be 
presented to users through push message, alert, notification or suggestion based on their patterns. 
 
Using big data is one of the major breakthrough, which saw the advancement since 2014, and seeing how 
businesses frequently practice this method and yet there were some reactions predicted happening in the year 
after, that is 2015 (Chen et al., 2014). Attempting better approaches to delivering innovative teaching and 
learning could expand potential outcomes and break up limits through the utilization of big data analytics. In this 
section, we assess some considerations of big data from the aspects of innovative teaching as discussed above 
(learning, teaching, social, and technology). 
 
LEARNING RESOURCES 
Figure 2 below shows the business process on how big data can become an alternative approach to offer 
innovative teaching for higher learning institutions. Learners generate data from many sources such as 
smartphones, social networks, LMS and so on. At the same time, an institution has data and records of students 
from their systems (library information systems, academic records, financial records, etc.). All the data sources 
are analysed and thus forming a profile for each and every student. 
 
 
Figure 2: Big data and innovative teaching process 
TOJET: The Turkish OnlineJournal of Educational Technology – November 2016, 
Special Issue for INTE 2016
Copyright © The Turkish Online Journal of Educational Technology 1213
 
A student’s profile is based on real data of the student’s behaviour. Big data analytic can come up with 
prediction about their teaching and learning preference like content materials, subject preferences, library visit 
habits, and others. Understanding the student’s profile based on the students’ habit can provide them 
personalization of services. A student can receive alerts from the big data systems when there are deviations 
from his usual pattern. For instance, a student can get notifications on his smartphone when he has not visited the 
library for some time, or has not downloaded any journal articles for a couple of days, or never participated in 
any online discussions about the subject. 
 
INNOVATIVE SOCIAL 
The advancement of online learning has introduced many aspects of innovative teaching competency including 
student learning activities. Students can work through the same online activities, distribute online surveys, share 
web-based searches, interact in online discussions, solve case studies, watched online videos, and published 
articles on the web. In addition, conversations from social networks as one of big data’s source could improve 
the trust of the community at large from reading the positive comments. Social media analytics are the analysis 
of data from social media channels such as blogs, Facebook, twitter and forums, which are used to analyze the 
stakeholders’ responses on the new course being introduced. This is much faster as compared to using a 
conventional method, such as waiting for the survey’s reports. 
 
INNOVATIVE TEACHING 
The innovation in teaching has improved from class-based activities where students are occupied with note-
taking activities to searching for rich content materials, which is easily more accessible than before. However, 
when students get a huge amount of data then it is very challenging to refine the materials within the time 
constraint. Here, big data offers the ability to extract value from a big volume and large varieties of data, and 
within the velocity of analytic, this could help students utilize those resource knowledge. In order to successfully 
apply big data in innovative teaching, support from the stakeholders should be involved. The Government need 
to fund more research regarding this matter. In addition, researchers and educational practitioners need to start 
providing more data into the media, social networks and any websites so that big data can analyse the valuable 
information. Similarly, higher learning institutions need to invest into big data analytic for innovative teaching. 
 
INNOVATIVE TECHNOLOGY 
Big data can be used to detect future trends and issues such as educational technology trends and product 
preference. This is due to its capabilities to gather data at a massive speed and able to distinguish the value of the 
data. For instance, with the aid of data generated from smartphones like social networks or IM (Instant 
Messaging) or news updates, higher learning institutions have the capability to understand the preferences and 
expectations of stakeholders including its students. It also provides useful insights on how the organization 
should improve their educational technology component. Data analytics can come from the students’ behavioural 
patterns based on their clicks, comments, sharing contents, and conversations (Anshari et al., 2015). Another 
notable benefit of data analytics is that the organization will be able to know their students’ preferences and 
personalities as well as predict what their next expectations might be, hence can consider fulfilling the 
stakeholders’ expectations. Big data can also be used to detect future problems in education systems like 
innovative teaching delivery. Big data can gather data at an immense speed and is able to distinguish the value of 
the data. Moreover big data, when captured, formatted, stored and analysed, can help an organization to gain 
useful insights to improve its operation, especially to education institutions and society. 
 
CONCLUSION 
The explosion of big data emerges from variety of data sources. This includes excessively using the Internet. Its 
benefit can be extended to higher education institutions in terms of offering innovative teaching experiences to 
their students. The adoption of big data in innovative teaching has been a promising new experience to the 
students, education providers, instructors, and the community. In fact, learning technological innovations will 
continue to have major effects on teaching approaches over the coming years. It provides useful guidelines for 
incorporating big data into innovative teaching from different perspectives for guiding teachers in effective 
integration of big data into teaching and learning. Thus, being more attractive to motivate and engage learners, 
the pedagogy, social interaction and technology could become the critical components of an innovative enhanced 
learning environment. It is important for higher learning institutions to explore the future impact of deploying 
big data by extending the learning experiences to students. In short, with their efforts to integrate pedagogical 
skills and big data approach, this model is supposed to enhance the development of innovative teaching 
performance for teacher as an insightful standard of making curriculum design in teacher education in particular 
and for schools in general. 
 
TOJET: The Turkish Online Journal of Educational Technology – November 2016, 
Special Issue for INTE 2016
Copyright © The Turkish Online Journal of Educational Technology 1214
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TOJET: The Turkish Online Journal of Educational Technology – November 2016, 
Special Issue for INTE 2016
Copyright © The Turkish Online Journal of Educational Technology 1216
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