An Insightful Review on Educational Big Data Analytics in Cloud-based e- Learning System
Ganeshayya Shidaganti1, Prakash S2, Srinivasa K G3, Anita Kanavalli4
1Research Scholar, Visvesvaraya Technological University, Belagavi, Assistant Professor, Department of C.S.E, Ramaiah Institute of Technology,Bengaluru, Karnataka, India
2 East Point College of Engineering And Technology, Bengaluru, India.
3National Institute of Technical Teacher Training & Research, Chandigarh, India
4 Ramaiah Institute of Technology, Bengaluru, India
Abstract
With the rapid momentum of progress in the methodology of knowledge delivery system in existing cloud-based e-learning system, there is also a rising awareness of the complexity of the upcoming educational big data in present times. In current era of cloud computing, there has been various research-based implementations toward leveraging analytical operation along with presence of wide ranges of analytical tools. However, there is no much reported tool to offer educational-based big data analytics. This problem has been addressed by various researchers with an aid of conceptualized, analytical, qualitative, and quantitative models. Therefore, the contribution of the proposed manuscript is to perform an exhaustive review of big data-based approaches as well as research-based analytical tools presented by various researchers in the form of advantages and limitation as well as exploration of open research issues.
Keywords: Analytics, Big Data, Education, Learning Management system, e-leaning, Data Mining, Knowledge discovery.
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1. Introduction
Electronic learning or E-Learning is one of the cost-effective mechanisms of imparting knowledge by crossing various impediments e.g. distance, time, and location [1]. In fact, adoption of e-learning has been witnessing a faster progress with an advancement of the time. These calls for collection of different form of data e.g. course materials, students and instructor information, textual-feedback, schedules, etc [2]. Some of the information are quite static while majority of the information are being dynamic. The dynamic set of information will consist of interaction among the students or between student-instructor [3]. The existing online Learning Management System (LMS) e.g.
Moodle, Massive Open Online Course (MOOC) not only offers a massive ranges of courses but also offers a productive priviledge of accessing significant insights on the different forms of data[4-5].
With an evolution of cloud computing, storage is never a problem, but offering ubiquitous features in e-learning system will also call for exponential increase of education data. Such forms of massively increasing data will eventually take the shape of the big data where data are characterized by voluminous problem, heterogeneity problem, uncertainty problem, and many more.
Unfortunately, such data cannot be saved in conventional storage model for which reason implications of conventional analytical tools cannot be applied on the top of this. There has been significant number of research work towards exploring an effective analytical operation on Big data because such approach is the best solution to confirm better cost saving, effective reduction of processing time, initiate novel development of business avenue and products, clear visualization of global market trends, and predictive analysis[6-10]. However, such forms of development are always associated with significant impediments too e.g. storing complex forms of education big data, discovering implicit and explicit knowledge of educational data, minimizing computational complexities associated with analysis (offline/online), offering significant scalability, providing data security, etc. Till date, it was only believed that big data are generated from sensory applications
only [11-16], but with the usage of future internet architecture, it is more likely that non-sensor based data offers more complexity in performing analysis. The education-based data can be generated in many ways where some are generated by user and some are generated by system in terms of log-reports. There is a fair possibilities that any of such education data could offer significant information that could assist the institutions or education service provider or even a student to understand the effective factors that has either positive or negative impact on their performance score. Hence, such application demands to be supportive of highly dynamic ranges of time, lesser dependencies of computational resources, cost effective operation involvement, user- friendly interpretation, a and the most important of all is to offer a precise recommendation. A precise recommendation is an outcome of sophisticated operation that are being carried out considering various ranges of analytical operation on it. Hence, there is a great opportunities and scope in education-based big data analysis. At present, there are various review work to discuss about many big data analytical techniques but very less work has been found to discuss or promoting about education-based big data analysis.
Therefore, this manuscript offers a comprehensive analysis of some of the signatory work being carried out toward education-based big data analytics in order to measure the degree of effectiveness in existing research techniques. The structure of the complete paper is as follows: Section 2 discusses about the big data approaches in e-learning system followed by discussion of big data analytics in Section 3. The discussion of education-based big data tools are carried out in Section-4.
This discussion is followed by briefing of open research issues as a significant contribution in Section-5 and conclusion in section-6.
2. Existing e-Learning Solution
Adaptive Content Manager
Classification manager
Component Manager Knowledge
Discovery
Learning repository
Assessment Resource
mgmt/monitor Content
mgmt
Collaboration
Cloud Management System
Cloud Educational Services
Physical machines
Conventional LMS Cloud-based LMS
Figure 1 Difference between Convention LMS and cloud-based LMA
With reference to the conventional cloud-based architecture in Figure 1, it can be seen that apart from data storage, cloud computing offers multiple privilege for its users i.e. i) resource management/monitoring, assessment and evaluation, content management, content delivery, and collaboration unit with an aid of multiple number of physical machines offered by the service providers itself [17]. The most significant contribution of cloud computing is its offering of different forms of services viz. Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), Platform-as- a-Service (PaaS) [18]. Usually, SaaS is used by students and instructors while IaaS is used by technical members (e.g. web-master, supervisor, administrators, etc.). The contributions of cloud- computing in e-learning system are as follows viz. i) Simpler Installation, ii) Superior Accessibility, iii) Cost-Effective, iv) Scalability[19-20]. As the data/services in cloud-based e-learning offer independent of conventional physical server, and installation process can be initiated by any user just by downloading the application of offered e-learning system. Owing to cloud-based environment,
the accessibility of any forms of data is always high. Although, there is more process of maintenance in cloud-based application, it offers value added services with less downtime that cannot be ascertained by the conventional e-learning system. Another significant contribution of using cloud- based e-learning system is its flexibility of bandwidth management. Non-cloud based e-learning invites all its traffic directly to its application server with a finite capability limitation. However, such forms of capability can be increased dynamically while migrating to cloud-based e-learning system. Apart from this, there is a higher degree of security offered by its service providers and hence complete security is the responsibility of cloud-service provider sand not much by the user.
3. Contribution of Big-Data in e-Learning
Adoption of Big Data offers a significant positive impact on the student as it revolutionizes the mechanism of teaching-learning experience. Following are the potential beneficial factor of adopting Big Data approach in educational services viz. i) it offers a potential insight about the learning ability of the students and assists them in realizing the essential part of their study practices, ii) it can specifically tell the students which part of the topic / study area will need special focus within the study materials, iii) it also offers significant insight about the frequently adopted courses to know the trend in better way by using a collaborative networks, iv) the delivery of the data as well as knowledge-based services are almost instantaneous with higher accuracy increasing the productivity of knowledge delivery services in educational domain, and v) by adoption of machine learning approach, Big Data offers comprehensive prediction of performing and underperforming students.
All these beneficial features are not only limited to students but it also offers significant benefits to the instructors too viz. i) unbiased feedback of the student’s performance also assists to assess the delivery system of the instructor, ii) it assists in developing highly customized training contents based on exact demands of the students, and iii) it also targets to implement a comprehensive strategies of e-learning goals [21-22].
3.1. Existing Research-using Big Data Approach
In recent times there has been evolution of different forms of approaches towards implementing big data approach on the data captured from e-learning applications. The significance of Big Data based approach towards educational field was emphasized by [23]. A productive discussion of the architecture of educational big data was presented by [24] that discusses involvement of different forms of users, education data, preprocessing data, and reposition of educational data. The authors have promoted their discussion over public cloud system. The implementation of the big data approach over the educational data was frequently studied with respect to various tools as well as models too. Implication of different existing models (e.g. Hadoop, Spark, and Samza) was carried out by [25] with more focus on the theoretic aspect with less direct implication towards educational sector. Relevance of teaching methodologies using Big Data approach was discussed by [26]
offering importance of incorporating Information Technology within teaching management and using highly skilled resources. Similar form of conceptualized modeling work is carried out by [27- 33] where a case-study of certain university has been carried out.
There are also reports of implementation work towards educational big data. The problem associated with prediction is considered as one of the critical problem while applying big data approach. It was found that the most frequently used MOOC was assessed to offer good predictive role. This problem was investigated by [34] where Gradient Boosting Decision Tree has been utilized for increasing the prediction accuracy as compared to other frequently used prediction model e.g. support vector machine. The study was mainly implemented to predict dropouts. A simplistic architecture of the big data has been discussed by [35] considering a case study of specific stage of higher education. An interesting approach for facilitating personalized e-learning experience has been put forward by [36].
The authors have presented a discussion that adoption of big data approach along with Internet-of- Things and machine learning has good level of potential to strengthen the knowledge delivery methodologies in educational sector. The development of this framework was carried out using sentiment analysis, identification of set of activities of user, advisory for content delivery, forecasting the performance of the student, leveraging the network demand, and provisioning network resource, etc.
There are also offbeat forms of studies in big data approach that are quite different from the mainstream of implementation in educational sector. One of such problem was to consider crowdsourcing that is all about aggregation of essential information for facilitating some specific utilization of knowledge. [37] Have introduced an incentive-based policy to leverage crowdsourcing policies. The authors have utilized an optimization principle to increase number of participants.
Another unique idea formulation has been formulated by [38] where the authors have developed a laboratory prototype for enhancing the analytical skill of big data. The authors have also discussed about the clustering methodologies, modeling of topic, execution of recommendation system, etc.
Some of the different forms of studies have found to report the usage of multimedia contents for analysis of educational big data. A non-conventional study has been formulated for investigating such contents in e-learning system using machine learning for facilitating an effective extraction process of feature [39-40]. This solution can be claimed to solve the classification issues during performing prediction. In short, this study contributes to extract information associated with extracting time duration spent to view the educational video contents in e-learning system. At present, there are adoptions of various forms of visual analytical-based approach for analyzing education big data. The recent work carried out by [41] has presented a visual analytical scheme of geo-tagged educational data. The model has also implemented a layer-wise approach along with clustering technique in order to learn various forms of learning patterns. The idea offers a better control over the geo-tagged data from one user-interface of visual analytics in order to perform in- depth investigation of personalized behavior of learning. Hence, there are various amounts of studies towards educational Big Data by considering different challenges associated with it. Figure 2 highlights the frequently addressed problems with its methodologies and limitations.
Study based Enhancing
teaching- learning
experience Personalization Discovering pattern from
educational data Prediction, classification
Qualitative
Conceptualized
Experimental
Gradient Boosting Decision Tree
machine learning Visual analytics
Association rule mining, MapReduce
No extensive classification
No numerical analysis No
benchmarking Narrowed complexity
of educational data .
Problems Methodologies
Limitations
Figure 2 Observation of Existing Research using big data approach
4. Analytics in e-Learning
The usage of analytics plays a crucial role in big data approach on e-learning system. The prime motive of adopting Big Data analytics is to ensure a better examination process of large scale data with good possibility of latent correlation, complex patterns within it, and some sort of potential business information in different shape. However, the major dependencies of applying analytical operation are to work on different forms of tools and sophisticated technologies to perform discovery of precise knowledge. It basically assists the instructor to obtain various significant information
associated with their performance and real-time demand. In order to configure better form of examination benchmark as well as construct strategy of assessment, an analytic plays an important role in e-learning management system. The mechanism of performing analytics has now become distributed with an evolution of distributed mining algorithms [42].However, it is never an easy task to performing analytical operation on future state of cloud-based e-learning system as there are different sources of evolution of data with different ranges of complexities and thereby making the shape of the data quite challenging enough to be subjected to existing analytical operation. Basically, the prime intention of any research work towards analytics over e-learning system is to disclose the latent information about the knowledge delivery services. For this purpose, different forms of tools are utilized e.g. knowledge mapping, predictive tools, machine leaning, etc. The outcome of any form of learning analytics are i) forecasting of upcoming trend of information related to the progress/performance of student, ii) evolution of new pattern, iii) better recommendation system [43]. At present, there are 4 types of analytics used in educational services e.g. descriptive analytics, predictive analytics, prescriptive analytics, and diagnostic analytics [44]. The above mentioned analytics are the generalized classification where more specific form of classification of analytics used in e-learning will be i) learning-based analytics and ii) academic-based analytics[45]. The learning-based analytics is basically used for analysis over course-level data as well as departmental level data, while the Academic-based Analytics is used for different objects e.g. regional, institutional, national and international level. Apart from the above classification of educational big data analytics, there are also good possibilities of other forms of analytical process e.g. off-line analytics [46], business intelligence analytics [47], real-time analytics [48], memory-based analytics [49], massive analytics etc. Each of these analytics has its own specific utilization and characteristics. Theoretical representation of big data analytics methods are also categorized on the basis utilization of classification, prediction, clustering, time-series, and association rules.
4.1. Existing Research using Analytical Approach
Analytics plays a crucial role in Big Data approach for the educational data. It assists to offer different forms of the knowledge discovery process over different forms of data that could be possibly unstructured, semi-structured, as well as structured. The outcome of any analytical operation and its corresponding implementation is basically a unique knowledge that offers value added information for the given set of educational data and services over cloud. At present, different forms of the analytical approach has been implemented considering educational data.
There are various reported research works promoting the benefits of adopting big data approach over learning management system. According to [50], there are various research approaches for learning analytics on the basis of the behavior of student along with their performance. The authors added that encouraging such techniques will definitely build up a good recommendation system. Therefore, one of the significant contributions of the big data analytical approach is to offer an efficient recommendation system on the basis of the discovered knowledge. It was found that incorporation of context in formulating recommendation system offers better design in analytical process. According to [51], the performance of a recommendation system could be significantly improved if contextual- information has been considered. Such methodologies offer more interesting connection between the learning performance and predictive outcomes. The study carried out by [52] has used factorization- based techniques with an aid of collaborative filtering process over educational data. The study outcome was found to offer better predictive performance in terms of accuracy. Research work towards evolving up with predictive approach was also witnessed in the work of [53]. The study has attempted to predict the performance of the students over certain integrated courses over Moodle with an aid of regression. The study is more-or-less carried out using both qualitative and quantitative approach where the outcome of study performs prediction of performance of students on the basis of offered courses in Moodle. Exactly similar form of study was also carried out by [54]
with a difference of inclusion of personalization features while performing analysis of student’s performance. An unique study towards learning analytics was initiated by [55] where the researcher have implemented Bayes algorithm, k-means clustering, factorial analysis and have also considered the design aspects of the social network. Another unique study was reported by [56] that is meant for investigating the reason of behavior of students over specific e-learning system. The study uses various distinct behavior of student to frame up learning analytics.
Apart from predicting or analyzing the student’s wise performance, there are also reported works on academic-wise performance analysis. Such forms of work could only be carried out if a successful design implementation is carried out for extracting individual student’s information carefully. [57]
Have accumulated educational data from educational sector for the sole purpose of streamlining management roles and responsibilities. The idea is majorly focused on improving the backbone support for any academics. A unique direction of research towards analytics was also explored with respect to adoption of social network. According to [58], construction of learning analytics can lead to better forms of learning methodology that directly improves analytical performance. The study outcome was assessed using conventional centrality metric of social network analysis along with statistical performance parameters. There are also studies carried out towards ensuring security of the analytical data in similar domain. According to [59], incorporation of privacy preservation over the analytical data could render better degree of data accessibility and sharing operation.
Introduction of visual analytics is another increasing trend on big data analytics considering e- learning system. There are various reported studies to promote the applicability of visual analytics.
The work of [60] has introduced a mechanism for investigating the learning patterns of complex forms from multimedia contents of MOOC. The study outcome was proven to offer significant information of various clickstreams for the given multimedia contents. Overall, the study has contributed to understand the learning pattern as well as behavior of online students in MOOC. Such forms of the studies are also realized in order to explore the hidden capabilities of the students.
Emphasis of developing visual analytics was also carried out by [61] using data from MOOC.
Research work in such direction was carried out by [62] where a diagnostic-based analytical approach was developed for assessing the performance of the students score online. The study has potentially assisted to compute the grades of respective courses enrolled by the students. Further study towards visual analytics was carried out by [63]. The authors have considered the case study of mining the contextual information associated with the software engineering in order to develop the learning analytics. According to [64], visual analytics also assists in exploring better interleaving spaces in existing e-learning system. Hence, it can be seen that there are many research work carried out considering MOOC, which absolutely doesn’t say that MOOC is standard framework but rather it highlights presence of challenges in MOOC design. According to [65], the problems of MOOC could be improved by developing a structured feature for handling massive volumes of education data. For this purpose, the authors have introduced a visual analytical tool where learning analytics could be implemented over MOOC. Same author have also carried out analysis towards self- regulated learning [66]. Therefore, various work has been carried out towards developing an effective big data analytical approach on educational data. Figure 3 highlights the frequently addressed problems with its methodologies and limitations associated with analytical approach
Problems Methodologies
Limitations context-based
recommendation
Prediction of Student performance
Analyzing student’s action
social networks
Learning pattern
Collaborative filtering
Matrix factorization Regression
Bayes algorithm, k-means
Visual analytics
experimental
Data complexity not considered
Works on static data only
Data context is not considered No
benchmarking
Narrowed scope
Figure 3 Observation of Existing Analytical Approach
5. Study of Big Dataset and Tools in Education
Existing research works are carried out towards publically available dataset. However, these datasets doesn’t bear a typical characteristics of big data. At present, there are various datasets that are used in big data analysis viz. leisure dataset, web analytics dataset, graphs and network dataset, financial dataset, weather dataset, medical dataset, machine learning dataset, etc. Big data, [67]. This data are available in multiple formats e.g. XLS, CSV, DWG, ECW, GeoJSON, JSON, KML, Mr.SID, SHP, Web Feed, etc. However, a closer look into the existing dataset shows that they have a finite size and shape of the data and they are devoid of any artifacts or dynamicity. Existing studies are carried out considering all these as the test dataset in order to carry out analysis over big data problems.
However, it should be noted that such data are still a static data of finite size and it doesn’t have any characteristics that a data should bear while traversing over cloud networks in real-time. In present times, there has been evolution of various tools to facilitate analytical operations on Big Data. Some of the commonly known tools are: Cassandra, Hadoop, Plotly, Bokeh, Neo4j, Cloudera, OpenRefine, Storm, Wolfram Alpha, RapidMiner, etc. Big Data Made Simple, [68]. The beneficial features of these big data analytics are viz.
Faster identification of errors / risk / pitfalls.
Instant tracking of novel strategies for a given analysis.
Potential improvement of service.
Offers significant cost saving.
In-line with customer’s anticipation.
However, there are some reported problems in the existing big data analytics too. The first problem with existing big data analytics is that it has dependencies of sophisticated computational capabilities and resources. A simpler way to understand this is incapability of standard Hadoop to perform sophisticated analysis based on real-time problems [68]. The second problem of existing big data analytics is that they should be used in very different and non-conventional manner which is not only time-consuming but also calls for uncertainties. Some of the big data analytics that are reported to offer good supportability of real-time problems are:
SpaceCurve: This tool is capable of performing analysis over massive data with a matter of a second.
Cloudera: It is an open-source tool and offers capability to perform interactive-mode of analysis for data reposted in Hbase system Apache Storm.
Storm: This belongs to a reputed social networking enterprise Twitter that is capable of performing computation in highly distributed manner over real-time data. Apart from its fault tolerant feature, Storm is also known for its simple usage with lesser dependencies on coding skills Klass data.
Gridgrain: It is another open source big data analytics developed in Java and supports Hadoop platform. It is considered as better substitution of Map Reduce. It is capable of analyzing grid data with higher scalability and comes with better in-memory support system GridGain.
Apart from the above mentioned names, some more tools e.g. HANA, Mongo DB, Greenplum are also known to offer different forms of big data analytical operation. Although, there are many other list of big data analytics, but the above mentioned names of tools used for big data analytics are considered as frequently used tools as seen in majority of the existing research works. However, there are no dedicated big data tools for carrying out knowledge discovery for educational data. It has been observed that same tools are used by majority of the researchers performing investigation on knowledge discovery process for the given set of educational data. However, there are some good attempts for exploring better possibilities of big data analytics in favor of educational streams as well as existing learning management system. Some of the educational-based big data analytics are:
SmartKlass: It is an open-source technology that offers virtual knowledge delivery process using its user-friendly dashboard. It also offers a plug-and play usage reducing
installation troubles. It can be used for corporate training, as well as for institution for performing various forms of analysis.
Learning Analytics Enriched Rubric: It is basically technique to offer a sophisticated grading mechanism as per the outcome of any assessment on the basis of well-defined criteria. It is used with Moodle to understand the learning behavior of student on the basis of multiple set of interactive information Learning Analytics Enriched Rubric.
BrightBytes: It is basically a predictive form of analytics and can perform enhancement of learning performance.
Engagement Analytics: For a given set of indicators, this tool is known to offer information about the measurable progress made by students. It is also capable of identifying a specific or a set of activities that has positive impact on student’s performance in Moodle.
Majority of the above mentioned educational big data analytics have some common characteristics operation viz. i) it analysis rate of completion of undertaken courses by the student, ii) it keeps a consistent monitoring of the underlying performance as well as progress made by the student, iii) it manages all the scores obtained from scheduled assessment, iv) it also stores all the data obtained from the student during random as well as defined survey about their experience and anticipation, and v) it performs obtaining specific responses in form of review or feedback from any active users.
All this information is aggregated by the existing education-based big data analytics in order to extract valuable knowledge from the archives of massive educational data. Although, the progress in evolution of educational analytical tool is slow but it is not stagnant either. There is always evolution of some other analytical tools that acts as plugin to further upgrade features.
6. Core Research Findings
There are various number of research work being carried out towards big data approaches in last decade (Figure 4) where it is used over various fields. However, there are very less proportion of work being carried out towards investigating it over educational domain. Although, there has been typical adoption of cloud-based services over revolutionizing the education system that also witnessed an exponential rise of educational data, but extensive study towards educational data using big data approach is quite less to find.
Figure 4 Research Trend of Last Decade 6.1. Essential Findings
In order to ensure a proper applicability of big data approach over the educational domain, the most challenging part of the implementation is to confirm that the input for the experiment should have all the characteristics of big data. However, this is one of difficult part of the investigation and hence existing researchers are bound to carry their analysis with publically available big dataset. The
complete process of big data implementation is a complex process and needs a specialized skill to understand and interpret the complete process. Therefore, the adoption of big data approach demands the users as well as clients to understand the complete implementation process for better inference. At the same time, extracting the specific data from the educational data is quite challenging as the data are highly unorganized and stored in local database system which are absolutely not subjected to any form of contextual data integration in cloud. This results in two problems viz. i) owing to its unstructured form, it cannot be stored or accessible by standard database system and ii) conventional analytical operation cannot be performed combining all educational data that is actually in its heterogeneous form. Because of this problem, there is always a demand of a highly skilled expertize which is also an expensive implementation. Finally, the potential challenge is associated with the organizational barrier which doesn’t let the integration as well as synchronization of the data in effective manner for facilitating a flexible analytical operation.
Apart from this, the current investigation [69] shows that higher concern related to affordability and accurate usage of big data over education while data inaccuracies as well as privacy. Hence, there are quite a number of challenges associated with implementing big data analytics over educational domain in order to strength the teaching learning experiences. However, there is a need of lot of research attempts for witnessing signification improvement.
6.2. Open Research Issues
Till the previous section, it has been seen that there are presence of various types of big data analytical tools with varied ranges of capabilities. Although, there are less number of dedicated analytical tools for education stream, but presence of other available tools e.g. apache hive, Spark equally assists in performing analysis. There are also some good research attempts towards educational big data analytics that are associated with both advantageous features and limitations.
However, this section will highlight more information about the problems that have been yet found unaddressed in existing system:
Lack of emphasis on Response Time: Irrespective of different capabilities of existing analytical tools, different educational data bears different degree of complexities that potentially affect the response time. At present, existing research work was not been reported in the direction of reducing the computational time during knowledge discovery process. Hence, it can be said that existing studies are more focused as well as applicable for offline analysis and quite less for online analysis.
Non-incorporation of data complexity: There are many reported work which has stated problems in existing analytical tools e.g. Hadoop and Map Reduce. Hence, majority of the existing research work are found to have direct dependencies on a defined dataset which cannot be termed as Big Data. If an investigated data doesn’t bear the 5V properties of Big data, it cannot be termed to be addressing the data complexity problems.
Collaborative Platform Missing: One of the significant distinctions of e-learning and cloud-based e-learning system is the presence of collaborative platform in the latter.
Presence of collaborative platform offers better form of interaction among the various users whose data, if acquired, can be of highly valuable importance for mining. Unfortunately, majority of the existing tools are connected to social network application which is a third party, where the educational data bears higher degree of complexity that is quite unnecessary. This problem could be addressed be developing an in-built collaborative tool to perform similar action.
Ignorance of Security: Majority of the existing education based big data analytics works on conventional security protocols with authentication very similar to normal applications. At the same time, they are also developed using open-source components which invites higher level of security risk from any form of attackers.
No attempts of Benchmarking: It is highly essential that implemented solution of existing technique should be subjected to certain benchmarking tools or data. Unfortunately, the research work on existing educational based learning analytics are so much diversified in terms of their outcomes that none of the solution was proven to offers universal acceptance with scope of tools. This reduces applicability and increases risk of adoption.
7. Conclusion
The biggest problem about the conventional mechanism of knowledge delivery system depends on various tedious, manual, and uncertain way to understand the effectivity of knowledge acquisition as well as delivery system. However, cloud-based e-learning system leads to an evolution of a platform where this problem of scaling the effectiveness of knowledge delivery system can be automated.
However, the biggest impediment in doing this are many where majority of the problems and research challenges converges to data complexity itself. Hence, this paper reviews the existing literatures and explores the advantages and limitations of the all the signatory research-based implementation towards education-based big data analytics. The contribution of proposed study are in the form of core findings of the existing review e.g. i) more presence of hypothetical concept and less presentation of experimental concept towards educational analytics, ii) evidence of applicability is either limited to some specific application or not discussed at all, iii) existing research work emphasized very less on data complexity, iv) many assumptions towards data storage has not been discussed, iv) studies more inclined towards using Hadoop and Map Reduce, without rectifying the problems reported by other researcher. Moreover, the biggest pitfall found in existing research work is considering educational big data as input to the presented model. At present, the existing publically available datasets do exists for big data but there is no report of educational based big data. Therefore, our future work will rather focus on developing a framework that generates true educational big data where its complexity of storage and structuring will be first addressed unlike existing approaches. It will be then followed by developing of a cost effective smart analytical tool that can offer better form of educational big data analytics.
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