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Predictive Analytics in a Higher Education Context
Abstract
Predictive analytics is the process of forecasting future courses of action by analyzing historical and current facts. It's now a priority in many organizations because it can suggest the most favorable future planning by letting decision makers combine data about the four W's - that is, what, who, where, and when - to analyze why and how. Apart from business, it plays a vital role in higher education planning. Higher education plays an important role in a nation's socioeconomic development. As an educational management tool, predictive analytics can help improve education quality by letting us analyze critical issues in education such as enrollment management and curriculum development. This article presents an analytical study of the prospects of predictive analytics in education planning, focusing on technical education. As a case study, the authors discuss an All India Council for Technical Education-sponsored project at Delhi Technical University, India.
... Data collection is the first step in the predictive analytics process (Jindal and Borah 2015). ...
... Collected data must be preprocessed that is cleaned, transformed, and integrated before it undergoes the training process (Jindal and Borah 2015). The aim of data cleaning is to identify the problems and errors and correct the errors in the dataset. ...
... Validating the model is important to measure its accuracy. A high rate of accuracy indicates the maximum prediction accuracy (Jindal and Borah 2015). Most of the previous studies report how well the regression model performed in terms of R-squared values. ...
Weather prediction is one of the challenging issues around the world. It is necessary to determine the effective use of water resources and forecasting weather-related disasters. The emerging machine learning techniques are coupled with the large set of weather dataset to forecast weather. Rainfall depends on a lot of weather attributes. The dataset may have relevant and irrelevant attributes. In this paper, two supervised learning algorithms are proposed to forecast the weather. In the first method, selected features are fed into the multiple linear regression model for training. Then, the prediction is performed with good accuracy of 82%. In the second method, to reduce the error rate of the deep learning algorithm we need to encode the cyclical features before applying the deep learning algorithm. Then, tuning hyperparameters in the n-hidden-layered networks improved the performance of the model with good accuracy of 92.32%.
... Educational Data Mining (EDM) is one of the applications of data mining techniques that aid individual learning from educational software, collaborative learning through computer support, computer-adaptive testing and analyze the factors that affect the student's performance. Some of the issues that are facing by education system are prediction of instructor performance [7], prediction of students performance [8][9][10][11][12][13][14][15][16], enrollment management and curriculum development of students [17], experience of students learning [18][19][20][21][22][23][24], problems in handwritten coursework [25], analysing careers of students [26] , improper education process [27] and low quality of educational process [28,29]. To have a convenient education system, many researchers have established different techniques to solve the limitations. ...
... In 2015, P. Kaura et al. [14] predicted the final performance of the students using data from a collaborative geometry problem-solving environment. Furthermore, predictive analysis of students based on the enrollment management and curriculum development was proposed in 2015 by J. Rajani and D. B. Malaya [17]. Later in 2016, A. Elbadrawy et al. [8] forecasted grade of students based on recommender systems which were capable of analyzing the performance of the students in a timely and accurate manner. ...
... The 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}} 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}} 3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}} B.2 Clusters of Experts for all students at α = 0.98 ...
Evaluation of student academic performance usually consists of several components,
each involving a number of judgments often based on imprecise / fuzzy data. This
imprecision arises either in numeric data or from human (teacher/tutor) interpretation of
human (students) performance. Arithmetical and statistical methods based on Aristotelian
two valued logic and Bayes probability approach, have been used for aggregating
information from these assessment components. Although the evaluation of student’s
performance is a very complex system, these methods have been accepted by many
educational institutions around the world.
In the study, it is argued that methods of classifying and grading students academic
performance using traditional techniques might not necessarily be the best option to
evaluate human acquisition of knowledge and skills. A Human does not think in numbers
and, many a time, take decisions on their perceptions. Therefore. reasoning based on the
perception of domain experts using fuzzy models could provide an alternative way of
handling various kinds of imprecise/fuzzy data.
The complexity in modeling students academic performance is further compounded due
to several constraints apart from their academic test score. These include: conducting
examinations in different settings, performance shaping factors of students, and alike. It can
be stated that performance assessment is a dynamic process that produces numeric and
non-numeric data, from which a set of reasonable conclusions are expected that could
impact on students learning outcomes. A high-quality evaluation system could provide
grounds for individual student improvement, and might ensure receiving a fair grade so as
not to enhance her/his future opportunities. To conduct a review at a defined interval is,
therefore, a desirable practice.
Examinees and examiner play a pivotal role in the educational grading system.
Perception of experts in evaluating students answer script is of vital significance. Multiple
experts evaluating student’s academic performance involves epistemic uncertainty which
can be modeled using fuzzy set theory. The highlight of the research study are summarized
below:
• All twenty evaluators agree to one another considering the similarity measures on
fuzzy sets. The computed Fleiss Kappa coefficient is 0.74 which signifies an excellent
agreement between all the twenty experts.
• 11 out of 20 evaluators are similar in their decision making of students academic
performance with possibility (�-level cut, 0.98). The inter-rater reliability �-coefficient among the selected 11 teachers is 0.41, which signifies a fair/moderate
agreement in the evaluation process.
• Based on Zadeh-Deshpande formalism for evaluating students answer scripts using
the concept of the reliability of information (degree of confidence) via the ‘degree
of match’ and fuzzy inference system in students performance evaluation, it can be
inferred that the overall performance of all the students is ‘Average.’ Furthermore, 206
/ 237 students (87%) are declared as ‘Average’ with a high degree of certainty by the
evaluators (teachers). The aim of the proposed method is not to replace the traditional
method of evaluation. Instead, the proposed technique is a step forward to enrich the
present system of student’s performance assessment.
• Traditionally, academic ranking of student’s performance is based on test score which
can be interpreted in linguistic terms such as very good, good, poor, very poor with
varying degree of certainty attached to each description. There could be several
students in a school having very poor performance with varying degree of certainty.
The authorities would certainly like to improve students academic performance based
on their ranking. The case study relates to the combination of Zadeh-Deshpande
formalism with Bellman-Zadeh method to arrive at an optimal ranking of especially
very poor students based on well-defined performance shaping factors.
• In this thesis, the issue of optimal ranking of performance shaping factors using
‘Dempster-Shafer’ theory of evidence have been addressed. No definitive conclusion
can be made with such a limited study.
Computing with Words (CW or CWW) is a system of computation which offers an
important capability to compute with information described in natural language.
Application of fuzzy logic- Level 1 complexity in CWW with a case study in academic
performance of secondary school students is presented in this thesis.
Keywords: Education grading system; examinee and examiner; experts perception;
fuzzy sets; fuzzy sets; fuzzy relational calculus; similarity measures; cosine amplitude
method; inter-rater reliability; Kappa coefficient; probability density function; degree of
match; fuzzy inference system; degree of certainty; Students ranking; goal; constraints;
decision; Zadeh- Deshpande formalism; Bellman-Zadeh fuzzy decision-making model;
belief; ranking of factors; Dempster-Shafer evidence theory.
... Data analytics is the process of analyzing data in order to interpret patterns and gain insights (Rajni & Malaya, 2015). Long used in business, data analytics' potential in higher education has grown as institutions adopted enterprise resource planning (ERP) systems to house immense amounts of past and current student data (e.g., Nguyen et al., 2020). ...
... Bernacki et al. (2020) highlights a common thread among the data analytics subfields: the desire to develop analytical methods capable of predicting the future based on known information (predictive analytics; Gandomi & Haider, 2015;Nandal et al., 2017). Predictive analytics can aid decisions in areas such as enrollment management and curriculum development (Rajni & Malaya, 2015). Other examples include predicting which students may receive a D, fail, or withdraw from a course (Dorodchi et al., 2020;Smith et al., 2012) and which students may be retained or leave a university (Murtaugh et al., 1999). ...
About one-third of college students drop out before finishing their degree. The majority of those remaining will take longer than 4 years to complete their degree at “4-year” institutions. This problem emphasizes the need to identify students who may benefit from support to encourage timely graduation. Here we empirically develop machine learning algorithms, specifically Random Forest, to accurately predict if and when first-time-in-college undergraduates will graduate based on admissions, academic, and financial aid records two to six semesters after matriculation. Credit hours earned, college and high school grade point averages, estimated family (financial) contribution, and enrollment and grades in required gateway courses within a student’s major were all important predictors of graduation outcome. We predicted students’ graduation outcomes with an overall accuracy of 79%. Applying the machine learning algorithms to currently enrolled students allowed identification of those who could benefit from added support. Identified students included many who may be missed by established university protocols, such as students with high financial need who are making adequate but not strong degree progress.
... Predicting learning and learning outcomes from educational data is an important objective, bearing the potential to generate new insights for education and practice. Indeed, the issues of the use of predictive analytics and the implications thereof in shaping critical issues is highlighted by Rajni and Malaya (2015), who note that predictive analytics can "help improve the quality of education by letting decision makers address critical issues such as enrollment management and curriculum development" (p. 24). ...
... Whereas deep learning has demonstrable merits, the requirements for building robust models and their limited interpretability at present constrain their usefulness for the study of education, teaching, and learning. Finally, to realize the potential benefits of machine learning and deep learning depends in large part on the data, as such, it will be crucial to continue to recognize the challenges of predictive analytics, especially in relation to the quality and quantity of data (Rajni and Malaya 2015). ...
Large swaths of data are readily available in various fields, and education is no exception. In tandem, the impetus to derive meaningful insights from data gains urgency. Recent advances in deep learning, particularly in the area of voice and image recognition and so-called complete knowledge games like chess, go, and StarCraft, have resulted in a flurry of research. Using two educational datasets, we explore the utility and applicability of deep learning for educational data mining and learning analytics. We compare the predictive accuracy of popular deep learning frameworks/libraries, including, Keras, Theano, Tensorflow, fast.ai, and Pytorch. Experimental results reveal that performance, as assessed by predictive accuracy, varies depending on the optimizer used. Further, findings from additional experiments by tuning network parameters yield similar results. Moreover, we find that deep learning displays comparable performance to other machine learning algorithms such as support vector machines, k-nearest neighbors, naive Bayes classifier, and logistic regression. We argue that statistical learning techniques should be selected to maximize interpretability and should contribute to our understanding of educational and learning phenomena; hence, in most cases, educational data mining and learning analytics researchers should aim for explanation over prediction.
... Accordingly, learning analytics tools and techniques are essential for educators, students, and administrators to help enhance learning and teaching (Aldowah, Al-Samarraie, and Fauzy 2019), and to provide a foundation on which to enact change. In turn, predictive analytics draw on past and current data to make predictions about trends (Rajni and Malaya 2015) that can assist academic advisors to be pre-emptive in their efforts to improve student success. This data can also provide lecturers with real-time insight about student performance, which in turn can inform teaching design and activities (Gavriushenko et al. 2017;Siemens and Long 2011). ...
At South African universities, the role of academic advisors is not easily defined and there is limited literature about the attributes and characteristics required of those who occupy advising positions in the country. This article proposes a model for identifying academic advisors for South African higher education contexts. The authors adopt a phenomenological approach to construct this model. Based on existing literature and data collected from advising interactions with students, the authors share and explain their model for identifying suitable candidates for academic advisor roles in South African higher education contexts. The model consists of seven dimensions: cultural quotient, data analytics, personal/ emotional support, psychosocial and socioeconomic support, scholarly activities and networking, skills formation and support, and teaching and learning. The authors conclude by emphasising the importance of academic advising for South Africa, before making recommendations for using the model to identify suitable candidates for advisor roles.
... Beyond HR, AI-based predictive analytics has been applied in several other fields. Jindal and Malaya (2015) explore how predictive analytics is used in higher education for tasks such as enrollment management and curriculum development, demonstrating its versatility across different sectors [15]. Piccialli et al. (2021) discuss AI's application in healthcare, where predictive models are used to forecast medical bookings, improving patient care and resource allocation [16]. ...
Workforce planning is a critical component of talent management, essential for aligning organizational needs with human capital to achieve efficiency, productivity, and long-term success. Traditional workforce planning methods often rely on historical data and reactive approaches, which are insufficient in today's dynamic labor markets characterized by rapid technological advancements and evolving workforce expectations. This paper explores the integration of Artificial Intelligence (AI) and predictive analytics into workforce planning as a proactive solution to these challenges. We propose a comprehensive framework that incorporates AI-driven predictive analytics into workforce planning processes. The framework focuses on three key areas: skills gap analysis and workforce forecasting, dynamic workforce allocation, and proactive succession planning. By leveraging AI, organizations can accurately identify emerging skills gaps, optimize resource utilization through real-time workforce allocation, and enhance succession planning by predicting leadership readiness. The paper develops key propositions demonstrating how AI can enhance talent forecasting and workforce management. Through AI-driven models, organizations can make data-driven decisions, improve talent retention, achieve operational efficiency, and enhance agility and responsiveness to market changes. We also discuss the strategic implications of adopting AI in workforce planning and address the challenges organizations may face, such as organizational readiness, data quality issues, skill gaps in AI and analytics, and ethical and privacy concerns.
... Machine learning algorithms analyze extensive data to identify patterns and predict future outcomes [28,29]. In education, predictive analytics forecast a student's performance, learning trajectory, and potential challenges [126][127][128][129][130][131]. Early warnings allow educators to intervene and provide additional support, addressing learning gaps and enhancing overall student success. ...
... Predictive analytics makes use of learning algorithms, various statistical modelling techniques, and data mining technologies in order to draw inferences from the data and predict trends and behaviors based on the data [9]. The utilization of these technologies has been groundbreaking, resulting in the processes of digital transformation that have had sweeping impacts across software testing, educational management, and business operations [10]. In the medical area, predictive analytics can change the face of patient care by forecasting infectious disease outbreaks, tailoring treatment plans, and employing hospital resources with more effectiveness. ...
... Machine learning algorithms analyze extensive data to identify patterns and predict future outcomes [28,29]. In education, predictive analytics forecast a student's performance, learning trajectory, and potential challenges [126][127][128][129][130][131]. Early warnings allow educators to intervene and provide additional support, addressing learning gaps and enhancing overall student success. ...
This research paper explores how the integration of Artificial Intelligence (AI) in the education sector is bringing about transformative changes, particularly within the frameworks of Education 4.0 and 5.0. In response to the evolving technological landscape, education is undergoing a shift to address the challenges of the 21st century, moving away from traditional models to embrace more personalized and adaptive approaches. Education 4.0 represents a significant shift where technology, notably AI, is harnessed to enhance the learning experience. The paper investigates the utilization of AI technologies, such as machine learning algorithms and natural language processing, to create personalized learning environments. These environments are designed to meet the specific needs and preferences of individual learners, fostering a more engaging and effective educational experience. The move from Education 3.0 to 4.0 signifies a departure from standardized, one-size-fits-all approaches to education, embracing a more dynamic and responsive system. Expanding on the principles of Education 4.0, Education 5.0 takes the integration of AI in education a step further by emphasizing adaptive learning. The paper delves into the concept of adaptive learning, exploring how AI systems can dynamically adjust instructional strategies based on real-time feedback and learner progress. Education 5.0 seeks to optimize the learning journey by tailoring content, pace, and assessments to each student's abilities and learning style, aiming to improve overall educational outcomes. Additionally, the research examines the challenges and ethical considerations associated with the widespread adoption of AI in education. It critically evaluates issues related to data privacy, bias in AI algorithms, and the potential impact on teacher-student relationships. The findings emphasize the need to strike a balance between technological innovation and ethical considerations, ensuring the responsible and effective integration of AI in personalized and adaptive learning environments.
Keywords: Artificial Intelligence, Education 4.0, Education 5.0, learning, Industry 4.0, Medical education, Engineering education.
... The significance of predictive analytics is inevitable in many fields because this technique helps to predict the future about the particular field information [2]. With the help of predictive analytics the information about the future business performance can be elucidated, therefore; the organizations utilized predictive analytics technique to understand the complete growth, to improve the current scenario and to learn about the development possibilities etc., [15]. Besides, it is utilized for identifying trends, relationships, and patterns within data that can be used to monitor the future event and behavior [3] [8]. ...
Now-a-days, Predictive analytics is one of the most important Big Data trends. Prescient Analytics is the accumulation of extensive, mostly unstructured data from various sources. The mixture of various information sources, for example, online networking information, climate and traffic are improved by internal information is especially basic. But both predictive analysis and data mining attempt to make divination about possible events in the future with the help of data models. Predictive Analytics processes utilize various statistical strategies such as machine learning or neural networks, regression and extrapolation to perceive in the information patterns and infer algorithm. These algorithmic procedures are assessed depends on test data and optimized data. It is to be noted that as data availability increases the accuracy of the algorithm also improved. By chance if the improvement procedure is finished, the algorithm and the model can be connected to information whose classification is obscure. Predictive analytics model capture connection between various factors to assess chance with a specific set of conditions to distribute a score or weight age. Successfully, on applying predictive examination the organizations can adequately explain huge information for their benefit. We present a detailed survey on Data mining and predictive analytics here, by analyzing 15 techniques from standard publishers (IEEE, Elsevier, Springer etc.,) of the year from 2008 to 2018. Based on the algorithms and methods utilized which are inconvenient, so the problems are analyzed and classified. Moreover, to indicate the improvement and accuracy of all the research articles is also discussed. Furthermore, the analysis is carried to find the essential for their approaches so that we can develop a new technique to previse the future data. Eventually, some of the research issue is also inscribed to precede the further research on the similar direction.
... Besides, since there were many approaches used in data classification, the decision tree method was selected for the application. Rajni and Malaya [14] presented ideas about the utility and availability of EDM to solve the engineering educational planning problem. They chose decision trees and ANN methods as prediction models. ...
Educational data mining (EDM) is an important research area which has an ability of analyzing and modeling educational data. Obtained outputs from EDM help researchers and education planners understand and revise the systematic problems of current educational strategies. This study deals with an important international study, namely Trends International Mathematics and Science Study (TIMSS). EDM methods are applied to last released TIMSS 2015 8th grade Turkish students' data. The study has mainly twofold: to find best performer algorithm(s) for classifying students' mathematic success and to extract important features on success. The most appropriate algorithm is found as logistic regression and also support vector machines-polynomial kernel and support vector machines-Pearson VII function-based universal kernel give similar performances with logistic regression. Different feature selection methods are used in order to extract the most effective features in classification among all features in the original dataset. "Home Educational Resources", "Student Confident in Mathematics" and "Mathematics Achievement Too Low for Estimation" are found the most important features in all feature selection methods.
... The analytics tools are used in various industries, such as education, health, and engineering. Some institutions of higher learning employ predictive analytics tools to predict students' performance based on current academic results, as well as course enrolment [4,69]. The predictive analytics tools are used in the health sector to predict patients' health conditions based on their history as well as current health status, lifestyle and behaviors [1]. ...
... We can observe the principle of the system (see Fig. 4). Predictive analytics main goals reside on the production of relevant information, actionable insight, better outcomes, and smarter decisions, and to predict future events by analyzing the volume, veracity, velocity, variety, and value of large amounts of data [14]. ...
The planning of education in Morocco represents an essential element in the projects implementation of the educational system, on which rests the various operations of diagnostics, realization and evaluation of the educational strategic choices. The planning profession has benefited very well from technological advance, and the country has been in the process of automating information systems for a long time. But according to our analysis, the education information system will be able to be more effective if it can adopt the techniques proposed, especially with regard to the establishment of an Integrated Information System (IIS) which groups operational systems, then use a Decision Support System (DSS) to help decision makers. As well as an Early Warning System (EWS) to predict problems, and a Recommendation System (RS) to propose realistic and effective measures. The unification of such systems will improve both the quality of the educational data management and the educational administration processes.
... Higher education sectors (HESs) play a vital role in a nation's overall social and economic development. In turn, several factors contribute to establishing a quality HES, including goal-based processes, curriculum relevance in terms of discipline-specific subjects to meet business and industry needs, and the effective delivery of teaching and learning activities (Rajni & Malaya, 2015). HES are also recognized as an important contributor to technology-driven social advancement (Al-Shaya et al., 2012). ...
Higher education systems (HES) have become increasingly absorbed in applying big data analytics due to competition as well as economic pressures. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. In this paper, the authors conducted a systematic mapping study to address this deficiency. The qualitative and quantitative analysis of the mapping study resulted in highlighting the research progression over the last decade, and identification of three major themes, 12 subthemes, 10 motivation factors, 10 major challenges, three categories of tools and support techniques, and 16 models for applying big data analytics in higher education. This result contributes to the ongoing research on applying big data analytics in HES. It provides a better understanding of the level of contribution to research as well as identifies gaps for future research direction.
... The goals of predictive analytics are to produce relevant information, actionable information, better results, more intelligent decisions and predict future events by analyzing volume, veracity, speed, variety, and value of large amounts of data (Rajni & Malaya, 2015). ...
With the desire to stay at the forefront of competitiveness and improve decision-making, many companies are currently focusing on using predictive analytics to make strategic business decisions. Predictive analytics is a category of data analysis intended to make predictions based on historical data to predict the future using analytical techniques such as statistical Modeling and machine learning or deep learning. Using predictive analytics, an organization can detect trends and adapt its policy to whatever may happen. This article will explain the different aspects of predictive analytics and provide real-life scenarios that can be beneficial for businesses.
... Adding the Neural Network and decision tree provides better results with a small accuracy difference. A case study was conducted in the study [13] by Delhi Technological University. Another RS was developed using a Neural Network with the decision tree technique, and the Weka and SPSS Clementine Predictive tools to determine the accuracy level in the predictive analytics process that predicts enrolment decisions, future grades, and satisfaction level. ...
In Saudi Arabia, all high school graduates who want join local universities have to go through a preparatory year before selecting their specific specialization/major. One of the most concerning issues for those fresh undergraduate college students is the selection of their specialization. College specialization selection is critical for them, as their academic and career future will be affected by this decision. An un-suitable specialization selection will have unfortunate consequences, not only on the students' future but also on the university’s resources and budget. This paper sug-gests a solution to this problem by introducing a preliminary study of a recommend-er system (RS), which will recommend the appropriate specialization for the students based on various tests and grades during the preparatory year at King Abdulaziz University (KAU). The proposed system guides students through their specialization selection process based on their abilities. The collaborative filtering technique was used to build the RS and K-fold cross-validation was adopted to evaluate its accura-cy and performance. The results showed the prediction of a specialization for each student with good accuracy ratio. These promising initial results provide a feasible solution to assess this issue further in future studies.
... In [8], author(s) have discussed the importance of predictive analytics in education field by addressing fields such as enrollment management and curriculum development. Predictive analytics help organizations grow, compete, enforce, improve, satisfy, learn, and act. ...
... In [6], author(s) have discussed the importance of predictive analytics in education field by addressing fields such as enrolment management and curriculum development. Predictive analytics help organizations grow, compete, enforce, improve, satisfy, learn and act. ...
Predictive Analytics refers to forecasting the future probabilities by extracting information from existing datasets and determining patterns from predicted outcomes. Predictive analytics also includes what-if scenarios and risk assessment. Since not much work has been done on social network analysis using predictive modelling, therefore, in the current research work, effort has been made to use principles of Predictive Modelling to analyse the authentic social network dataset and results have been encouraging. The post analysis of the results have been focused on exhibiting contact details, mobility pattern and number of degree of connections/minute leading to identification of the linkage/bonding between the nodes in the social network.
... Predicting students performance is one of the most studied topics in Educational Data Mining.Several works use prediction techniques for predicting or classifying the performance or marks on exams, courses or scholar periods. In [7], applied predictive techniques over scholar data. They tested at Delhi Technological University data for classifying aspects like: students enrollment preferences, actual demand of certain courses, students that would like to be transferred, satisfaction level and future marks considering several factors. ...
... Yet another study aimed at the higher education category, specifically engineering education, propounded EDM as the apt model for that segment of education. This study used ANN and DT models as prediction models to forecast engineering entrance examination data and solve the engineering education planning problem (Rajni & Malaya, 2015). Other studies led to somewhat different conclusions. ...
Educational Data Mining (EDM) is an important tool in the field of classification of educational data that helps researchers and education planners analyse and model available educational data for specific needs such as developing educational strategies. Trends International Mathematics and Science Study (TIMSS) which is a notable study in educational area was used in this research. EDM methodology was applied to the results of TIMSS 2015 that presents data culled from eighth grade students from Turkey. The main purposes are to find the algorithms that are most appropriate for classifying the successes of students, especially in science subjects, and ascertaining the factors that lead to this success. It was found that logistic regression and support vector machines – poly kernel are the most suitable algorithms. A diverse set of features obtained by feature selection methods are “Computer Tablet Shared”, “Extra Lessons Last 12 Month”, “Extra Lessons How Many Month”, “How Far in Education Do You Expect to Go”, “Home Educational Resources”, and “Student Confident in Science” and these features are the most effective features in science success.
... Validation of the model is necessary to adapt them to the new data is to measure accuracy ( Figure 3). A high level of accuracy in the model illustrates the maximum predictive accuracy [27]. Predictive analytics techniques are divided into two groups [28]: ...
A revolution in computational methods and statistics to process and analyse data into insight and knowledge is along with the growth of data. The paradigm of data analytic is changed from explicit to implicit raises the way to extract knowledge from data through a prospective approach to determine the value of new observations based on the structure of the relationship between input and output (predictive analytics). In the cycle of predictive analytics, data preparation is a very important stage. The main challenge faced is that raw data cannot be directly used for analysis and related to the quality of the data. Completeness is arising related to data quality. Missing data is one that often causes data to become incomplete. As a result, predictive analytics generated from these data becomes inaccurate. In this paper, the issues related to the missing data in predictive analytics will be discussed through a literature study from related research. Also, the challenges and direction that might occur in the predictive analytics domain with problems related to missing data will be presented.
... The study also demonstrated significant results in case of CART. Jindal and borah (2015) applied diverse categories of decision trees including C5.0, C4.5-A1 and C4.5-A2 for predicting student achievement in higher education [5]. The researchers in addition employed neural networks on the same educational dataset for predictive assessment, and then afterwards, compared the results of different varieties of trees with neural networks with the purpose of achieving paramount significance in predicting the performance of students. ...
Ensemble methods and conventional base class learners have effectively been applied in the realm of educational data mining to ameliorate the accuracy and consistency in prediction. Primarily in the contemporary study, researchers conducted empirical results on pedagogical real dataset acquired from University of Kashmir, using miscellaneous base classifiers viz. j48, random forest and random tree, to predict the performance of students. However, in the later phase, the pedagogical dataset was subjected to more proficient version of stacking viz. stackingC, with the principle objective to ameliorate the performance of students. Furthermore, the dataset was deployed with filtering procedures to corroborate any improvement in results, after the application of techniques such as synthetic minority oversampling technique (SMOTE) and spread sub-sampling method. Moreover, in case of ensemble stackingC, hybridization of predicted output was carried out with three base classifier vis-a- vis j48, random forest and random tree, and the classifier achieved paramount accuracy of 95.65% in predicting the actual class of students. The findings have by and large noticeably corroborated that the stackingC classifier, attained significant prediction accuracy of 95.96% when undergone through undersampling (spread sub-sampling) and 96.11% using oversampling (SMOTE). As a subject of corollary, it calls upon the researchers to broaden the canvas of literature by employing the analogous methods to uncover the diverse patterns hidden in academic datasets.
... Predictive analytics can be considered as an educational management tool that will help academic decision makers address critical issues and plan educational services. The patterns and trends predict future trends, which can be used for effective planning [5]. Various studies in the application of data mining are being explored by several literatures. ...
Employability is the most challenging outcome of higher education institution. It is evident that there are a number of information technology (IT) graduates that failed to address the need of information technology industry, hence, result to job mismatch and high unemployment rate. This presents the Employability Predictive Model Evaluator using PART and JRip classifier algorithm. The study also aims to determine the significant attributes in the development of dataset for predictive model and generate a predictive model for employability of IT graduates using PART and JRip classifier algorithm. The 4- year taken courses of graduates from school year 2013-2016 and employability status extracted from e-Graduate Tracer Survey (eGTS) were used as dataset of the study. The academic courses from the university registrar were clustered based on Commission on Higher Education of the Philippines information technology courses which includes General Education, IT Common Courses, IT Professional Courses, IT Elective Courses and On-the-Job Training Grade. JRip generated two (2) rules while PART generated seven (7) rules, which were used as the logical conditions in the development of Employability Predictive Model Evaluator for IT graduates.
... If school administrators can better understand their students and teachers, then they can anticipate needs and make operational improvements based on likely future events. Jindal and Borah (2015) lay out a case for the use of predictive analytics in education. ...
Purpose
The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education.
Design/methodology/approach
Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education.
Findings
The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process.
Practical implications
Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes.
Originality/value
This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.
... The study also demonstrated significant results in case of CART. Jindal and borah (2015) applied diverse categories of decision trees including C5.0, C4.5-A1 and C4.5-A2 for predicting student achievement in higher education [5]. The researchers in addition employed neural networks on the same educational dataset for predictive assessment, and then afterwards, compared the results of different varieties of trees with neural networks with the purpose of achieving paramount significance in predicting the performance of students. ...
Ensemble methods and conventional base class learners have effectively been applied in the realm of educational data mining to ameliorate the accuracy and consistency in prediction. Primarily in the contemporary study, researchers conducted empirical results on pedagogical real dataset acquired from University of Kashmir, using miscellaneous base classifiers viz. j48, random forest and random tree, to predict the performance of students. However, in the later phase, the pedagogical dataset was subjected to more proficient version of stacking viz. stackingC, with the principle objective to ameliorate the performance of students. Furthermore, the dataset was deployed with filtering procedures to corroborate any improvement in results, after the application of techniques such as synthetic minority oversampling technique (SMOTE) and spread sub-sampling method. Moreover, in case of ensemble stackingC, hybridization of predicted output was carried out with three base classifier vis-a- vis j48, random forest and random tree, and the classifier achieved paramount accuracy of 95.65% in predicting the actual class of students. The findings have by and large noticeably corroborated that the stackingC classifier, attained significant prediction accuracy of 95.96% when undergone through undersampling (spread sub-sampling) and 96.11% using oversampling (SMOTE). As a subject of corollary, it calls upon the researchers to broaden the canvas of literature by employing the analogous methods to uncover the diverse patterns hidden in academic datasets.
... Data regarding excused and unexcused absences can be analyzed at multiple levels -school wide trends versus individual student trends, for instance. Moving beyond post hoc truancy monitoring, administrators can take preventative action by employing predictive modeling techniques that mine data and forecast outcomes [31]. These dashboard systems are able to monitor and predict absenteeism in a granular way, with predictability enhanced by the quantity and quality of input variables available when computing an estimated risk-factor score. ...
Vulnerable (e.g., LGBTQ, homeless, disabled, racial/ethnic minority, and/or poor) youth disproportionally report challenges at school compared to their majority counterparts, but we are not always sure of the best ways to support these students. How might big data help to ameliorate experiences for vulnerable students who are not part of the majority (e.g., White, middle class, straight)? We review current ways that using big data can promote student engagement specific to school experiences where vulnerable youth share a disproportional amount of burden. We review extant uses of big data to track, involve, and monitor student progress and attendance. Additionally, we review the potential privacy implications and threats to students’ civil liberties.
... Education and research play key role in nation's overall development. Quality education will be success oriented and fulfill better industry needs [5]. ...
Educational data analytics is used to study the data available in the educational field and bring out the hidden knowledge from it. Analytics is a process of discovering, analyzing, and interpreting meaningful patterns from large amounts of data. Data analytics reliesonthe techniques of data mining such as, classification, association, correlation, categorization, prediction, estimation, clustering, trend analysis and visualization. Predictive analytics can help in improving the quality of education by providing right information for decision makers to take better decisions. This paper focuses on the need for implementing the data analytics in educational system, suggests some strategies to use these needs. While implementing any system, the understanding of different components and their functions is necessary. In Educational management system, there are different components like technology, content, services, e-learner etc. This paper also discusses the key issues related with these components and their functions within the system such as services to be provided, content design criteria. The educational data analytics has potential to discover, analyze and predict meaningful knowledge from educational data which will help to education management system for flexible planning, execution and prediction for future.
The increasing demand for personalized learning experiences in education has led to the development of a Personalized Learning Analytics system powered by AI and enhanced with Randomized Algorithm Design (RAD). The system forecasts results, evaluates student performance, and offers personalized learning trajectories. RAD maximizes prediction accuracy through random selection of past data, ensuring for the purpose of offering objective analysis. Students can track progress and receive personalized feedback, and instructors can track class performance through individual interfaces. Career guidance is offered through an AI chatbot, and cloud scalability guarantees seamless operation.
Predictive analytical models have been applied in context of the Indian higher educational institutions dataset. This study is aimed to compare the predictive accuracy level of various machine learning algorithms (multi-class classification prediction algorithms) to assess the grading system applicable to a college based on various parameters outlined by NAAC (National Assessment and Accreditation Council). NAAC is an apex body to assess and accredit HEIs of India. An ensemble classification method is proposed to best predict the academic grading of university-affiliated colleges of India for the NAAC accreditation system. This maiden attempt for using PA (Predictive Analysis) on such institutional credential data would be beneficial to colleges applying for NAAC accreditation to judge their grade prior to final assessment. Thus they would be able to identify their strengths and weaknesses and eventually work towards further improving upon their grade. Such a system would pave the way for designing such predictive systems to assess the institutional rankings for several other assessment systems.
The integration of Artificial Intelligence (AI) into Human Resources (HR) analytics has become a focal point for organizational enhancement in Higher Education Institutions. This research paper delves into the multifaceted applications of AI in HR analytics within the context of higher education. Through a comprehensive literature review, we explore existing studies, identify gaps, and elucidate the pivotal role of AI in optimizing HR processes. Our research aims to achieve a nuanced understanding of how AI technologies are currently employed in HR functions within higher education settings. We investigate key areas such as recruitment, performance evaluation, and employee engagement to discern the impact of AI on organizational dynamics. By leveraging case studies and real-world examples, we highlight successful implementations of AI in HR analytics, emphasizing the outcomes and implications for higher education institutions. Furthermore, the paper discusses the benefits of AI integration, ranging from increased efficiency to data-driven decision-making, while also addressing the ethical considerations and challenges associated with these technologies. Looking ahead, we present insights into future trends and emerging technologies that are poised to shape the landscape of AI in HR analytics within higher education. Recommendations are provided for institutions seeking to harness the potential of AI in enhancing HR processes. In conclusion, this research contributes to the evolving discourse on the intersection of AI and HR analytics in higher education. By offering a comprehensive analysis of current practices, challenges, and future possibilities, this paper serves as a valuable resource for academia, administrators, and policymakers navigating the transformative journey towards AI-driven HR excellence in higher education institutions.
Pre-trained convolutional neural network (CNN) structures are considered as one of the emerging education management tools that can help improve the quality of education by allowing decision makers to manipulate important indicators. These indicators, which are categorized as student and institution specific factors, may influence student progress, retention or dropout rates. In this paper, we develop a deep learning model of predicting students’ satisfactions and their expected outcomes and associated early failures. The model can also predict dropout rates and identify the main baseline risk factors that influence such rates. The academic data of 12,000 students enrolled from 2018 in the Arab Open University student information system are used as CNNs training dataset to ensure that all institution levels are represented. Then, the trained network provides a probabilistic model that indicates, for each student, the probability of dropout. Based on the prediction model, the study presents an early warning system framework to generate alerts and recommendations to allow early and effective institutional intervention. Experiments are achieved by using the proposed dataset and the performance of our approach is considerably better compared to the competitive models in terms of training/validation accuracy and mean square errors.
Student desertion is one of the main social problems around the world. Consequently, to propose this issue, there are several studies under different circumstances or scenarios. For this reason, this research creates four datasets, which take the common variables of easy extraction to the academic process. These variables have been grouped under common characteristics such as general student profile information, admission process information, financial information, academic information, and academic performance information. Thus, the method used in this research is analytical, since it is intended to analyze each subset of data in order to identify the variables with the greatest impact on university dropout. As a result, have been identified the variables with impact on university dropout, For this, a neural network model has been implemented using Python and Keras. In conclusion, the research evidence that academic information is mostly related to college dropout related to university dropout, while admission, financial, and student profile information are not significant in detecting or predicting college dropout. However, with the data obtained, it has been shown that the prediction is not early but in many cases late, since the notes would have already been delivered to students. Therefore, future research is intended to identify the causes that originate academic problems.
Data is critical for educational institutions. Data helps to achieve better results in educational processes. While the concept of educational data has been in existence for some time, the term learning analytics (LA) has gained popularity as a crucial area of focus in recent years. The term LA refers to the systematic process of measuring, collecting, analyzing, and reporting data related to learners and their educational contexts. It can be said that there are four main elements of LA: data, analysis, report, and action. LA offers a range of benefits for education: Prediction of learner outcomes, personalized learning experiences, etc. Despite its benefits, LA raises important ethical concerns. In this chapter, the concept of learning analytics is introduced and its main dimensions are discussed.
The similarities of big data analytics (BDA) and the know-how required because of its highly specialised nature have made it challenging for many organisations in attempts to select the tools. The challenge is increasingly prohibitive. This paper presents a formulaic approach consisting of set of criteria and a model, for selecting big data analytics tools for organisational purposes. The analysis focused on examining and gaining better understanding of the strengths and weaknesses of the most common BDA tools. The technical and non-technical factors that influence the selection of BDA are identified. The outcome of this study is intended to guide selection of most appropriate BDA tools and increase their usefulness in improving organizations' competitiveness.
Quality of education is essential for the teachers and the learners, and it is also crucial for the analyst. The process of maintaining high education quality is critical and challenging too. Education is a very compound and complicated system which has a cause and effect relationship that is nonlinear. Thereby, present chapter explores the satisfaction of the students towards the higher educational institutions in Oman for sustainable society. The study was based on a primary data and a structured questionnaire has been designed to collect the data. The measurement model has been tested to measure the internal reliability. All the constructs have indicated high internal reliability as Cronbach's alpha, and composite reliability values have been higher than 0.70. When all the conditions for the measurement model have been met, structural equation modeling is conducted. The structural model has been analyzed by way of bootstrapping methodology. The consistent PLS bootstrapping found that certification most significantly impacts satisfaction followed by website satisfaction. The analysis of the bootstrapping exhibited that certification is the most significant variable which influence the satisfaction of the students followed by the website design. Along with the website and certification, it was suggested that the educational institutions need to concentrate on the placement policies which can allow the students to be placed at international companies. Educational institutions can look forward to international standards to examine themselves and improve their standards. The role of accreditation agencies is vital in ensuring that the quality is fulfilled and they maintain the same quality with regular interval checks on the accredited educational institutions.KeywordsEducationQualitySatisfactionStudentsInstitutionsCertificationWebsiteBootstrapping
Educational data analytics is used to study the data which is available in the educational institutions and bring out the insights from it. Analytics is a process of discovering, analyzing, and interpreting meaningful patterns from large amounts of data. Predictive analytics can help in improving the quality of education by providing right information for decision makers to take better decisions. This paper focuses on the need for implementing the data analytics in educational system, suggests some strategies to use these needs. While implementing any system, the understanding of different components and their functions is necessary. The educational data analytics has potential to discover, analyze and predict meaningful knowledge from educational data which will help to education management system for flexible planning, execution and prediction for future.
Chadli, HajarBikrat, YoussefChadli, SaraSaber, MohammedFakir, AmineTahani, Abdelwahed Currently, green energy is knowing a massive growth in the world with the growth of newer energy sources such as wind energy, hydro energy, tidal energy geothermal energy, biomass energy and of Corse the Solar energy which is considered the second biggest source of electricity worldwide including morocco. The production of electricity via these centrals requires optimization at the different conversion levels. To obtain electricity that meets the standards of the electrical grid (sine wave of frequency 50 Hz), the inverter remains the first element to design and build. The structures based on multi-level inverters have brought an undeniable advantage to alternative continuous conversion, especially in high power applications. In this article a new 7-level inverter architecture with only six switches is presented and compared along with the other seven level inverter topologies. To improve the performance of our proposed multilevel inverter, we used a digital sinusoidal Pulse Width Modulation (SPWM) strategy using the Arduino wich leads to further reduction of THD. In this paper, the inverter was tested using Proteus software and Matlab Simulink simulator for harmonic analysis. Then real-time implementation of inverter was tested for a resistive load.
This paper describes a comparative study between two advanced nonlinear controls strategies; the Sliding Mode Control (SMC) and the Fractional-Order Sliding Mode Control (FOSMC), in terms of both reactive and active powers to improve the quality of the energy injected into the distribution grid by the wind energy conversion system (WECS). This later is based on the doubly-fed induction generator (DFIG). The objective is to perform modeling and direct control of the (WECS). Firstly, the dynamic modeling of the different parts of the WECS is performed. Then, the second part of this work concentrates on the proposed nonlinear control laws that rely on FOSMC and SMC. Finally, the performance of those strategies has been simulated in the MATLAB/SIMULINK environment using two wind profiles. One of them is a real wind profile of Asilah-Morroco city to test the system robustness and dynamics as opposed to real conditions.
In academic and industrial research, writing a project proposal is one of the essential but time-consuming activities. Nevertheless, most proposals end in rejection. Moreover, research funding is getting more competitive these days. Funding agencies are increasingly looking for more extensive and more interdisciplinary research proposals. To increase the funding success rate, this PhD project focuses on three open challenges: poor data quality, inefficient funding discovery, and ineffective collaborative team building. We envision a Predictive Analytics-based approach that involves analyzing research information and using statistical and machine learning models that can assure data quality, increase funding discovery efficiency and the effectiveness of collaboration building. Accordingly, the goal of this PhD project is to support decision-making process to maximize the funding success rates of universities.
Job performance in educational institutions is a major parameter to decide its success. Numerous parameters such as teaching methods, family background, student’s interest, student–teacher interaction, etc., are responsible to support the decision-making process in organizational success. Job performance is mainly related to student’s and educationist’s performance. Thus, there is a need to keep an eye on parameters associated with both student’s performance and educationist’s performance. This paper aims to provide a comparative analysis of tools, techniques, parameters, and algorithms along with different challenges, associated with monitoring performance of students and educationists. Various educational organizations apply data mining tools to analyze the performance of students and educationists. There are various versatile data mining algorithms available to serve the purpose. Thus, it becomes important to select the appropriate algorithm in an appropriate situation. The literature so far focused on student performance for analyzing the organizational success. In this paper, both the entities; i.e., student and educationist are being considered. The work presented highlights the possible benefits to the students, educationists, and management.
The performance measurement of a great variety of enterprises is a highly complicated issue, especially taking into account that performance has a great many aspects and many variables which may, at times, be highly inconsistent with each other. The use of analytics and advanced machine learning promotes the decision-making process for each and every organizational structure. This paper combines the Balanced Scorecard and predictive analytics in order to assess the performance of a co-financed European Union program, which addressed 4071 Greek Small and Medium-sized Enterprises (SMEs) that requested funding. The application of predictive analytics tools and metrics in the available dataset of all addressed SMEs reveal the M5 Model Tree regressor to be an overall best prediction model for estimating the effect of the evaluation of companies’ funding proposals on their financial results after the finalization of the co-financed program.
Predictive Analytics is a progressive and excellent area of data analytics which guess some event, appearance or likelihood dependent on existing information. Predictive Analytics utilizes data mining methods with the goal to make forecasts about future occasions, and make proposals dependent on these expectations. The procedure includes an investigation of memorable information and dependent on that examination to foresee the future events or occasions. Presently a-days Predictive Analytics is the essential idea in the Mining of educational data. The principle point of this paper is to explore the introduction to Predictive Analytics, applications of Predictive Analytics, and analysis of some Predictive Analytics tools in mining of Educational Data.
This paper presents the Learning Scorecard (LS), a platform that enables students to monitor their learning progress in a Higher Education course during the semester, generating the data that will also support the ongoing supervision of the class performance by the course coordinator. The LS uses gamification techniques to increase student engagement with the course. Business Intelligence best practices are also applied to provide an analytical environment for student and faculty to monitor course performance. This paper describes the initial design of the LS, based on a Balanced Scorecard approach, and the prototype version of the platform, currently in use by graduate and undergraduate students in the fall semester of 2016–2017.
Educational Data Mining (EDM) is an emerging fieldexploring data in educational context by applyingdifferent Data Mining (DM) techniques/tools. It provides intrinsic knowledge of teaching and learningprocess for effective education planning. In this survey work focuses on components, research trends (1998to 2012) of EDM highlighting its related Tools, Techniques and educational Outcomes. It also highlightsthe Challenges EDM.
The use of analytics in higher education is a relatively new area of practice and research. As with any new area of practice, a variety of terms are adopted to describe concepts and processes. Each of these terms is being integrated into the literature, but a preliminary review of the analytics in education and practitioner literature revealed similar terms with different conceptual or functional definitions, as well as different terms with similar conceptual or functional definitions. The intent of this paper is to present the different descriptions of the various types of analytics being discussed in the academic and practitioner literature. Second, we propose a conceptual framework that depicts the types of analytics and their relationship to each other. Finally, we propose a synthesized set of definitions for analytics-related terms commonly found in academia.
Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc.
This article is categorized under: Application Areas > Business and Industry
The main focus of this work is to build a Decision Support Tool, EDU-OPT which is helpful for both students and education planners. From the student perspective, it helps a student in selecting the most appropriate branch from an array of disciplines offered. Since this prediction is based on skill level, social factors etc., a student is more likely to achieve academic excellence and therefore better his/her employability chances. An appropriate branch selected also improves the satisfaction level of the student with the branch. This is also reflected in the future grades prediction of the admitted student. From education planner's point of view, the branch prediction service helps an institution's estimate the number of students seeking admission in particular branch and therefore helps predict the approximate strength of student in the particular branch. Thus the more popular branches of the institution can be identified. Satisfaction level prediction can be used by the education planners to see if the admitted students are satisfied with the contents of the course being offered during the semester. The methodology used in this tool is Classification algorithms. Our starting point was to explore the different classification algorithms to select the most appropriate one for EDU-OPT. In order to improve time complexity and prediction accuracy levels the existing C4.5 was adapted. Two heuristic functions were applied, one to improve the time complexity and another to improve the prediction accuracy resulting in the development of two algorithms namely C4.5-A1 and C4.5-A2 We have found that for Branch prediction, C5.0 was the most accurate with a prediction accuracy level of 99.05. For predicting Satisfaction level the most accurate algorithm was C4.5-A1with prediction accuracy level of 60 (around) and finally, for predicting grade the most accurate algorithm was C4.5-A2 with a prediction accuracy level of 62. The algorithms were validated using standard weather dataset and then applied to AIEEE 2007(13,000 tuples), Delhi College of Engineering (100 tuples) and Assam Engineering College dataset (355 tuples) for the academic year 2009-2010. EDU-OPT were developed on Visual Studio 2008, Asp.Net 3.5.
The trend to greater adoption of online learning in higher education institutions means an increased opportunity for instructors and administrators to monitor student activity and interaction with the course content and peers. This paper demonstrates how the analysis of data captured from various IT systems could be used to inform decision making process for university management and administration. It does so by providing details of a large research project designed to identify the range of applications for LMS derived data for informing strategic decision makers and teaching staff. The visualisation of online student engagement/effort is shown to afford instructors with early opportunities for providing additional student learning assistance and intervention – when and where it is required. The capacity to establish early indicators of 'at-risk' students provides timely opportunities for instructors to re-direct or add resources to facilitate progression towards optimal patterns of learning behaviour. The project findings provide new insights into student learning that complement the existing array of evaluative methodologies, including formal evaluations of teaching. Thus the project provides a platform for further investigation into new suites of diagnostic tools that can, in turn, provide new opportunities to inform continuous, sustained improvement of pedagogical practice.
The Analytics tools are capable of suggesting the most favourable future planning by analyzing "Why" and "How" blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based "Analytics as a Service (AaaS)" the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.
This chapter is mainly crafted in order to give a business-centric view of big data analytics. The readers can find the major application domains / use cases of big data analytics and the compelling needs and reasons for wholeheartedly embracing this new paradigm. The emerging use cases include the use of real-time data such as the sensor data to detect any abnormalities in plant and machinery and batch processing of sensor data collected over a period to conduct failure analysis of plant and machinery. The author describes the short-term as well as the long-term benefits and find and nullify all kinds of doubts and misgivings on this new idea, which has been pervading and penetrating into every tangible domain. The ultimate goal is to demystify this cutting-edge technology so that its acceptance and adoption levels go up significantly in the days to unfold.
Educational Data Mining (EDM) is an emerging field exploring data in educational context by applying
different Data Mining (DM) techniques/tools. It provides intrinsic knowledge of teaching and learning
process for effective education planning. In this survey work focuses on components, research trends (1998
to 2012) of EDM highlighting its related Tools, Techniques and educational Outcomes. It also highlights
the Challenges EDM.
igher education is encountering unprecedented pressure for accountability from both internal and external constituencies. Frank Rhodes, the former president of Cornell University, has stated: "Accountability…is the newest buzzword for all institutions. It is an important—indeed, a vital—obligation, but it means very different things to different people." 1 These constituencies include legislators, the families of prospective students, accreditors, trustees, current students, faculty, and administrators—each wanting something quite different from the institution and each wanting the information for varying reasons and purposes. This pressure for accountability in higher education is actually nothing new; it has been a top concern for nearly 15 years. Today, however, the rising price of tuition is exacerbating the call for colleges and universities to demonstrate their effectiveness and to become more transparent about how resources are used. Higher education, meanwhile, has been extremely reluctant to step up to the challenge of measuring the outcomes of its teaching, learning, and research. Ironically, researchers can measure the movement of subatomic particles and the radiation and other effects of unseen nebulae, but when it comes to measuring and assessing the impact and effectiveness of teaching, learning, and research on campus, we all too often hear that such an effort is too difficult. Whether the difficulty is because we have not yet learned how to do this effectively or because we have merely avoided the task is irrelevant. Society is becoming increasingly intolerant of such responses, and political pressures are mounting for campuses to deal with these issues or to have the government do it for—and undoubtedly to—higher education.
This chapter examines the theoretical basis for data mining, one of the essential knowledge management processes, and uses a case study to describe its application and impact.
Measuring What Matters: A Dashboard for Success
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An IBM View of the Structured Data Analysis Landscape: Descriptive, Predictive, and Prescriptive Analytics
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Seven Reasons You Need Predictive Analytics
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Case Study: Integrating and Reporting in Institutional Research
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Western Carolina Utilizes Rapid Insight Tools for Enrollment Forecasting
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I-flex Lends a Hand to Education
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