Business Analytics in the Education Sector - Coursework Example

Business Analytics and Big Data Name: Institution: Activity 1: Business Analytics in the Education Sector From the elementary level to institutions of higher learning in both private and public sectors, the role of stakeholders in the education sector has been to develop the best mechanisms that can be used in improving the performance of students despite the ever-shrinking budget. The extent by which students improve in there learning is an essential determinant of the level of improvement that have been necessitated by different stakeholder in the education sector (Relander, 2015). The ability of learning institutions to engage in effective measurement of performance, management, and improvement of essential metric is the main objective of business analytics. The concept of Big Data is becoming increasingly associated with the education sector, especially in education technology. In the contemporary and highly modernized societies, portable technological devices such as the iPad and smart boards have replaced chalkboards and overhead projectors (Relander, 2015). For institutions of learning, the use of business analytics is based on the need to find methodologies that can be used in making the learning process more effective in terms of identifying the achievements and weaknesses of the learners while at the same time tracing academic progress and predicting future performance (Anibar, 2015). Other technological applications such as Cloud technology, mobile technology and different applications in these technological devices are laying role in shaping the future of modern education. This is an indication that data in this sector has changed the approaches that students in different learning institutions use in generating thought and in the acquisition of essential knowledge. In the education sector, the use of analytics has been perceived as a process of developing actionable insights through the definition of a problem, and the application of statistical techniques and analysis in the understanding of existing or simulated future data. In the education sector, therefore analytics such as big data have nee use in understanding the prevailing conditions and in using the available data to make predictions concerning a specific phenomenon, which is essential in improving the performance of students in the education sector (Relander, 2015). The role of big data as an approach in business analytics is to enable an understanding of different attributes concerning the students in the education sector. This is because this approach to analytics has been used in collecting data from students across the country. Data collected through this approach include aptitude test scores, which are essential in the development of the best approaches that can be used in improving teaching strategies and learning how to better engage the students (Anibar, 2015). Big data has been considered essential in improving the nature of services offered in the education sector because it allows for the collection of data from a wide range of students. The data collected is analyzed through an integrated system that can handle large amount of information and produce desired results. Through this approach to analytics it is possible for the stakeholders in the education sector to strategize on matter related to the education sector because the data generated is from majority of the targeted students and institutions hence a reflection of the prevailing conditions on the ground (Anibar, 2015). Figure 1.0 The Needs, Opportunities and Challenges of Big Data Analytics in the Education Sector In the process of developing data- driven classrooms, the introduction of business analytics in the education sector has allowed for the collection and analysis of the work done by students in a digital manner. The digital approach to the analysis of the work done by students is that it allows the schools system to track and report on the progress and performance of students in a timely manner (Relander, 2015). Through continuous tracking and reporting on performance, it is possible for the teachers and other stakeholders in the sector to consider the nest approaches that can be used in developing programs that suit the interests of different students based on their performance levels. In addition, it is through business analytics that programs which have been considered less beneficial in terms of enabling higher performance among students can be replaced with those programs that have the ability of improving on the performance standards of students (Lane, 2014). In the United States, the application of big data as an essential part of the education system has been applied not only at the district level but also at the state and federal levels. The collection and analysis of digital data has become an essential component of standardized testing. Business analytics in the education sector have also been applied at the classroom level in different institutions of learning and in different countries such as the UK (Box-Steffensmeir et al, 2014). This has been enabled through data and digital curricula, which has had an effect on the approach that instructors and teachers use in enabling learning among students (Lane, 2014). The Mastery Connect, which is a big data application, has revolutionized the way instructors and teachers grade their work and assign scores students (Relander, 2015). This application offers different features such as the bubble-sheet assignment application, which allow teachers to give assignments created in a tablet or a mobile device. Furthermore, it allows for the automatic transfer of scores and tracking of progress. The exportation and tracking of progress allows for the analysis of the progress made by students according to designed parameters (Relander, 2015). The introduction of business analytics such as big data in the education sector as part of learning in classrooms has created a platform for adaptive and personalized learning. There is an increase in the number of companies such as Knewton, which specialize in the development of educational technology (Relander, 2015). This company has specialized in the development of business analytics, which rely on predictive analytics and big data to expand the content to knowledge learned by students and the methodology used in the acquisition of the knowledge (Anibar, 2015). As an earning platform, Knewton technology provides an adaptive learning infrastructure, which helps teachers in the identification of the elements that students are not mastering and target the development of lesson plan modules that help students in developing mastery skills in the targeted areas (Anibar, 2015). In terms of growth and development of the education sector, the collection and analysis of big data is envisioned to become big business considering that technological companies such as ConnectEDU are in the process of establishing digital teaching and learning platforms, which can track the progress of students in different learning institutions (Relander, 2015). Insights acquired from the data gathers in this platform can then assist teachers in tailoring their instruction methods and resources with the objective of boosting the level of student engagement throughout the learning period (Lane, 2014). McGraw-Hill one of the leading textbook publishers on the international platform has been able to demonstrate the role of analytics in the education sector by embracing an adaptive learning system whose role would be to track the question and answer sessions in which students participate. Through this system, it has become possible for the publishing company to present appropriate learning resources whose objective is to provide answers to questions that students could not answer (Relander, 2015). Local learning institutions in the United States such as the Brookings Institution have been able to demonstrate the relevance of big data in the education sector through the introduction and the utilization of the warehousing system together with the student information system. Through this system, these institutions have been able to view a wide swath of student information in real time (Relander, 2015). These include information regarding the disciplinary profiles of the students and their levels of absenteeism in school. Through the analytics, it has become possible for these learning institutions at the local level to drill the available metrics to the level of an individual to ensure that the design of customized interventions is based on existing and available information (Relander, 2015). The United States government has also become an active participant in the implementation of big data in learning institutions. The US Department of education through the National dashboard has been able to use big data in the compilation of information from public school systems across the country. The main objective of this information is to acquire essential indication that can be used in the determination of the country’s ability to meet its 2020 eductaion objectives (Relander, 2015). The dashboard measures different indicators, which include the percentage of students within a specific age group with basic degree. The number of specific ages of students who have been enrolled in pre-school. In addition, through big data the national dashboard has been able to collect information related to the number of 18-24 year olds who have been enrolled in institutions of higher learning. This is inclusive of the measures of the number of students who have high proficiency in math and reading especially among fourth graders (Relander, 2015). The implementation of analytics in learning institution is based on the understanding that it has a role to play in promoting better learning in a short. Prior to the development of the best approaches that can be used in improving learning, it is the responsbility of the instructor to be engaged in the determination of the extent to which the students are responding to instructions. The role of analytics in this case is to provide the teachers and instructors with real-time data on the responses given by students on their methodologies of instructing and passing knowledge (Relander, 2015). Analytics embraces descriptive, inferential and predictive statistical methodologies in understanding the prevailing situation in a learning context provide prescriptions on the best approaches that can be used by the instructors to improve on the delivery of the intended objectives to the students and the prediction of possible behavior among students when the purported technique is applied. This s enabled through in-depth analysis of protocols that focus on the retention of students within a specific course and their progress in terms of ensuring high-level performance in the course (Anibar, 2015). The success of business analytics in the education sector is founded on the predictive analytics-reporting framework, which uses statistics in the identification, retention, progress, and completion of an understanding of the phenomenon under investigation. Through this approach it is possible to analyze different patterns which are related to performance among students and the relevance of different educational approaches in realizing the objectives of the eductaion sector (Ho et al, 2014). When conducting investigation using this predictive analytics it will be important to ask questions revolving around the enrolments of students in specific courses; aspects that keep students in school and the demographic variable, which affect behavior among students in learning institutions (Relander, 2015). The essence of analytics in the education sector also encompasses an understanding of the applications and relationship between educational data mining and learning analytics. These two elements are essential in answering questions related to the knowledge capacity of the students and the extent to which students are engaged in the learning process (Ho et al, 2014). In investigating the relationship between the two elements, it will be important to ask questions related to the relevance of short-term boost in performance in reading in improving the ability of students to understand different applications of words (Relander, 2015). Effective measurement of learning analytics and educational data mining is profound in online learning. This is because through such a platform it is possible for students to provide essential information that can be used to portray the phenomenon in context. Educational data mining and learning analytics are therefore applicable in situations where learning institutions and other stakeholder in the education sector are interested in modeling of user knowledge, behavior, and experience (Ho et al, 2014). Through this approach to learning it will be possible for learning institutions to engage in user profiling as a way of identifying the role that different demographics plays in providing an undertaking of the dynamics in the education sector. An additional area of application is in the use of educational data mining and learning analytics in the modeling of essential concepts in a domain and the key knowledge component within a selected domain (Relander, 2015). This is essenatil in understanding growth in the eductaion sector because it provides a platform for the analysis of the rtrends and developing applicable and practical solutions towards trends that may be damaging to the growth and development of the eductaion sector. In trend analysis, one of the outstanding features is to recognize change overtime. These changes are often in the form of the achievements and the challenge that the eductaion sector faces in terms of performance while operation under analytics. The information generated from this context varies depending on the area of focus and its relationship with the eductaion sector (Anibar, 2015). Activity 2: Big data analytics The concept of big data analytics does not just focus on the massive nature of a database but on its unstructured nature. This is because it is possible to generate the data from different sources such as humans and machines. Data generated by gunman beings include text messages from mobile phones, email conversations, documents drafted in Microsoft word and posts on different social media platform such as Facebook and Twitter (Henry & Venkatraman, 2015). Data generated from machines could include sensor information derived from automobile engines, video clips from surveillance cameras and location information derived from mobile phones. The distinguishing features between big data and the traditional methodologies of data generation is that the former is fast and generates massive amounts of data within a short period (Box-Steffensmeir et al, 2014). The challenge that most of the business entities and other institutions face is the analysis of the data in a timely manner to communicate information that is relevant to the prevailing phenomenon (Hargrave, 2013). The assumption that the process of generating big data is often through limited direct control often necessitates questions regarding the veracity of the data with regard to the level of accuracy and quality. This has led to the generation of questionable results. Ana additional element in understanding the relevance of big data is in its validity, which revolves around the extent to which the data generated can be used in addressing the situation at hand (Ferguson, 2014). The benefits of big data does not lie in the proliferation of information but on the analysis process which generates patterns that can be used in the decision making process. In terms of infrastructure, big data comprises of data analytics software, big data repository and data scientists (Henry & Venkatraman, 2015). Dimensions of defining and distinguishing big data Volume One of the attributes associated with big data is volume. This refers to the mass quantities of data, which organizations are trying to harness as a way of ensuring some level of improvement in their decision-making processes. In the volumes of data continue to increase at an unprecedented rate making it necessary for the organizations concerned to develop the most appropriate methodologies that can be used in eth management of data (Hargrave, 2013). Variety Big data exists in different types and are generated from different data sources. Variety in big data entails the process of managing complex and multiple data types. These include structured, semi-structured, and unstructured data types. Effective management of big data often requires organizations to analyze and integrate data from a complex collection of both non-traditional and traditional information sources (Hargrave, 2013). In addition, the organizations also have the responsbility of integrating and analyzing data from within and outside the organization since they both play a role in influencing the decision making process in the organizations. Technological improvements that have necessitated the development of variety of big data include the introduction of smart deices, sensors and social collaboration technologies which have necessitated the generation of countless forms of data in the form of texts, tweets, sensor data, video, log files among others (Hargrave, 2013). Velocity In big data, this refers to data in motion. In the contemporary society, technological developments have increased the speed in which data is generated, processed, and analyzed. An additional contributor to the high velocity of data is the real time nature of data generation, and the need to ensure that the decision making process in organizations is characterized by streaming data into the business process (Hargrave, 2013). The relationship between velocity and latency in big data is in the lag time when data is captured or created to the time when this data is made accessible. The rate of data generation in the contemporary society is relatively higher making it impossible for traditional systems to capture, store and analyze. Big data has also necessitated the development of time sensitive processes such as instant marketing and real-time fraud detection. The existence of such situations means that the data must be analyzed in real-time for its value in business to be understood (Hargrave, 2013). Veracity This entails the level of reliability associated with certain types of big data.one of the essenatil big data requirements of big data is in the realization of high data quality. Despite these requirements, it is important to note that even the best data cleaning apparatus does not have the ability of eliminating the inherent unpredictability of some data (Hargrave, 2013). Areas with high levels of unpredictability include customer behavior, the weather, and the economy. One way through which big data analytics has been able to ensure the development of balance is by planning for uncertainty. Through such planning, it has become possible for big data analytics to be used in the creation of opportunities that organizations can use in gaining high level competitive advantage in the digitalized market (Box-Steffensmeir et al, 2014). An additional benefit of big data analytics in the context of an organization is in its ability to enable businesses to improve on their interactions and the quality of services and products provided to the customers (Hargrave, 2013). The multiplicity of approaches and techniques that can be used in the management of big data provides a sufficient explanation why different organizations will take divergent approaches in building and engaging their big data capabilities (Hargrave, 2013). Figure 2.0 Big data dimensions Tools used in big data analytics The unstructured and massive nature of big data makes it relatively difficult for traditional data management systems to be used in the management of this data. Other data management systems such as Apache, NoSQL, and Hadoop often play a role in effective management of big data (Henry & Venkatraman, 2015). Hadoop for instance is an effective platform because it can be used in the storage and processing of big data on a cluster of relatively inexpensive servers. This platform is highly scalable considering that demands vary and has high resilience to failure because of its built-in redundancy (Ferguson, 2014). Hadoop has the ability of engaging in efficient processing of big data compared to parallel processors using the Google pioneered approach the Map/Reduce technique. Compared to traditional approaches used in the management and processing of data Hadoop is relatively cheaper because instead of costing thousands of dollars per terabyte, processing and managing big data through this system only costs hundreds of dollars per terabyte (Hargrave, 2013). NoSQL data stores serve as popular methodologies of data storage on Hadoop servers because they contain certain types of queries, which can be computed and analyzed in a relatively faster manner compared to the use of traditional SQL on relational databases. Compared to the traditional SQL, NoSQL does not operate on any standard databases because it has several versions that are available and can be used in handling different types and quantities of data (Henry & Venkatraman, 2015). Each of the variables types is considered advantageous because of their use is dependent on the big data problem that is to be addressed. Some of the common NoSQL databases include Neo4J, Cassandra, and Apache CouchDB (Ferguson, 2014). NoSQL and Hadoop databases are essential infrastructure that is used in the maintenance and processing of big data. Despite their presence in the market, there are emerging big data analytics vendors whose objective is to make sense of the existing big data and acquire additional insight to facilitate the process of making rapid and effective decision in different platforms (Ferguson, 2014). One of the most outstanding features of the emerging big data analytics software vendors is in their ability to develop visually appealing graphics whose role is to ensure the development of easier and more effective understanding of massive data volumes hence the development of platforms that necessitate better decision making processes (Hargrave, 2013). Big data and strong analytics capabilities Big data has the ability to create value an organizational context when it is integrated in the decision-making processes. For this to be realized it is important for such organizations to gain access into different kinds of data and develop strong analytics capabilities. These capabilities include the ability to acquire different software tools and the necessary skills in using the software (Henry & Venkatraman, 2015). Organizations engaged in big data analytics reveal that the relevance of big data is in the identification of a strong core of analytics, which is designed to address structured data. Analytics capabilities in these organizations are important because they facilitate a process through which these organizations can take advantage of the semi-structured data that can be converted into a standard form (Box-Steffensmeir et al, 2014). The use of core analytics such as query, data mining in the analysis of big data and reporting provides a platform for understanding the results based on a predictive modeling approach. Through these functional analytics capabilities, organizations can be engaged in a pragmatic way on interpreting and analyzing big data (Ferguson, 2014). For effective data mining, organizations engaged in the use of big data in their decision-making processes require advanced capabilities essenatil in finding configurations that define the inherent complexity. This requires the application of optimization models and improved analytics to facilitate the process of understanding how to transform essenatil business operations. This explains why such organizations use simulation capabilities in the analysis of multiple variables that are available within big data (Ferguson, 2014). There is an increase in the tendency of organizations to direct their initial big data focus to the analysis of structured data. This si based on the realization that there is need for big data to be used in the analysis of a plethora of data types and this explains why companies on the global platform have introduced techniques, which are essential in the analysis of texts in their natural state. This includes the capacity of interpreting and understanding the nuances of language such as sentiments, intentions, and slang (Henry & Venkatraman, 2015). Despite the success associated with big data analytics, different organizations are facing the challenge of using big data in the analysis of unstructured data such as geospatial location data and voice. The advantage however is that the technological world is experiencing improvements in terms of the development of hardware, software and skills which are considered essenatil in the collection, analysis and interpretation of unstructured data for the benefits of decision making in an organizational context (Hargrave, 2013). The emerging pattern in the adoption of big data and analytics and its relevance in business The process of understanding the relevance of big data landscape in an organizational context can be defined by the level of big data activities within an organization. There are different stages that define the process of adopting big data analytics along a continuum in the context of an organization (Henry & Venkatraman, 2015). Education is one of the essential stages, which focuses on the desire by organizations to build a base of knowledge on big data analytics. The eductaion process includes the understanding of the potential benefits of big data analytics. Furthermore, the eductaion stage also provides additional insights on the role of big data in unveiling important business opportunities within a specific market (Hargrave, 2013). The exploration stage involves the development of road maps on how big data can be used in solving essenatil business challenges. This would include the creation of a quantifiable business case and the development of a big data blueprint. This takes into consideration the existing data, skills and technology. At the engagement stage, the responsbility of an organization is to prove the business value of big data (Henry & Venkatraman, 2015). This includes the performance of an evaluation of the existing skills and available technology in terms of their ability to handle big data. Execution stage is the implementation of big data analytics as a platform that can be used in the transformation of the business in ways that will ensure that these organizations acquire some form of value in terms of improving on the decision making process (Hargrave, 2013). References Aggarwal, A. (2015). Managing big data integration in the public sector. Hershey, PA : Information Science Reference Anibar, S. (2015). Big Data Analytics in the Education Sector: Needs, Opportunities and Challenges. International Journal of Research in Computer and Communication Technology, Vol 3, Issue 11 Box-Steffensmeir, J., Hoy, C., Martin, W., Peters, E., Hattery, G. (2014) Development of a singular presence in data analytics for the ohio state university. Retreived from https://discovery.osu.edu/assets/documents/DataAnalyticsExecutive%20Summary.pdf Ferguson, R. (2014), Better decisions with smarter data, MIT Sloan Management Review, Retrieved from http://sloanreview.mit.edu/article/better-decisions-with-smarter-data Hargrave, B. (2013). Deans corner: volume, variety, and velocity: big data is here to stay. eNewsline Global Business Education News and Insights. Retrieved from http://enewsline.aacsb.edu/deanscorner/hardgrave.asp Henry, R., & Venkatraman, S. (2015). Big Data Analytics The Next Big Learning Opportunity. Academy Of Information & Management Sciences Journal, 18(2), 17-29. Ho, W.-S., Ng, A., Leung, K., Wang, F. L., & Wong, L.-P. ( 2015). A Teachers’ Survey on the Effectiveness of e-Learning Platform in a Higher Education Institution. Lane, J. E. (2014). Building a smarter university: Big data, innovation, and analytics. Albany : State University of New York Press, Relander, B. (2015). How Tech and Data Analysis is Shaping Education. Oxford University Press: Oxford Shattuck, K. (2014). Assuring quality in online education: Practices and processes at the teaching, resource, and program levels. Sterling, Virginia: Stylus Topi, H. (2014). Where is big data in your information systems curriculum?, ACM Inroads, v.4 n.1 doi 10.1145/2432596.2432601 Read More