The document discusses educational data mining and a proposed Student-Staff-Tutor (SSTT) framework. It summarizes the following:
1) Educational data mining uses techniques like machine learning, statistics and data mining to analyze educational data to better understand the learning process and student performance.
2) The SSTT framework models relationships between students, staff, and tutors and how these interactions impact student learning and outcomes.
3) An experiment applies clustering and social network analysis to educational data to analyze student knowledge distribution and interactions under the SSTT framework. The results found tutors play an important role in strengthening student-staff relationships and improving student performance.
It discuss about the Morrison teaching model in detail. It also discuss on understanding level of teaching - 1. Focus 2. Syntax and five types 3. Social system and 4. Support system in detail
MEMORY LEVEL OF TEACHING -HERBARTIAN APPROACHBeulahJayarani
It discuss about memory level of teaching - Herbartian approach in details. It explains the types of level of teaching, JOHANN FRIEDRICH HERBART - SIX STEPS OF HERBARTIANS ARE……1. Focus 2. Syntax - 3. Social system & support system in detail
It discuss about the Reflective level of teaching by Bigge and Hunt Teaching model. It also discusses on types of levels of teaching - how reflective level functions in 1. Focus 2. Syntax 3. Social system and 4. Support system in detail
It discuss about the Morrison teaching model in detail. It also discuss on understanding level of teaching - 1. Focus 2. Syntax and five types 3. Social system and 4. Support system in detail
MEMORY LEVEL OF TEACHING -HERBARTIAN APPROACHBeulahJayarani
It discuss about memory level of teaching - Herbartian approach in details. It explains the types of level of teaching, JOHANN FRIEDRICH HERBART - SIX STEPS OF HERBARTIANS ARE……1. Focus 2. Syntax - 3. Social system & support system in detail
It discuss about the Reflective level of teaching by Bigge and Hunt Teaching model. It also discusses on types of levels of teaching - how reflective level functions in 1. Focus 2. Syntax 3. Social system and 4. Support system in detail
The paradigmatic shift from a teacher-centered learning environment to a student-centered one is not an easy transition; and, does not occur effortlessly. What is student-centered learning? Necessary areas of change. Strategies for the shift. Positive outcomes. The paradigm shift. What changed? Teacher-centered vs. learning-centered instruction. 8 steps in the change process. Instructor concerns. Measurable objectives. Agent for change. Action plan.
1. From lower class to till college level all the students are doing Project method. By this PPt they can understand the procedure, steps, criteria for doing projects, merits & demerits
A Study of the Effectiveness of Self-Instructional Material (SIM) for Higher ...RHIMRJ Journal
The present study was aimed to find the effectiveness of Self-instructional Materials (SIM). Comparison of the
increased learning through SIM and through Direct Teaching was done in this study to know about the effectiveness of SIM at
Higher Education. The results did not show significant difference between two groups’ learning outcomes. However, various
factors are involved in learning activity. Factors like students’ attention, effectiveness of direct teaching etc. do affect the level
of learning. So, depending upon the above factors and quality of Self-instructional Material, the level of learning may differ.
It explains about what is mixed ability grouping, aims, mixed ability factors, strategies for teaching mixed ability classes, advantages, disadvantages in details.
This content consists of 'Andragogy and Assessment' presented by Ms Kalyani K., Assistant Professor, Vijaya Teachers College, Bangalore, in the webinar series 4 hosted by the Department of Education, Manonmaniam Sundaranar University, Tiruenelveli, Tamil Nadu.
This content consists of ' Assessment in Pedagogy of Education' presented by Dr. V. Sasikala Department of Education, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu. in the webinar series 4 hosted by the Department of Education, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu.
The paradigmatic shift from a teacher-centered learning environment to a student-centered one is not an easy transition; and, does not occur effortlessly. What is student-centered learning? Necessary areas of change. Strategies for the shift. Positive outcomes. The paradigm shift. What changed? Teacher-centered vs. learning-centered instruction. 8 steps in the change process. Instructor concerns. Measurable objectives. Agent for change. Action plan.
1. From lower class to till college level all the students are doing Project method. By this PPt they can understand the procedure, steps, criteria for doing projects, merits & demerits
A Study of the Effectiveness of Self-Instructional Material (SIM) for Higher ...RHIMRJ Journal
The present study was aimed to find the effectiveness of Self-instructional Materials (SIM). Comparison of the
increased learning through SIM and through Direct Teaching was done in this study to know about the effectiveness of SIM at
Higher Education. The results did not show significant difference between two groups’ learning outcomes. However, various
factors are involved in learning activity. Factors like students’ attention, effectiveness of direct teaching etc. do affect the level
of learning. So, depending upon the above factors and quality of Self-instructional Material, the level of learning may differ.
It explains about what is mixed ability grouping, aims, mixed ability factors, strategies for teaching mixed ability classes, advantages, disadvantages in details.
This content consists of 'Andragogy and Assessment' presented by Ms Kalyani K., Assistant Professor, Vijaya Teachers College, Bangalore, in the webinar series 4 hosted by the Department of Education, Manonmaniam Sundaranar University, Tiruenelveli, Tamil Nadu.
This content consists of ' Assessment in Pedagogy of Education' presented by Dr. V. Sasikala Department of Education, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu. in the webinar series 4 hosted by the Department of Education, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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Educational Data Mining is used to find interesting patterns from the data taken from
educational settings to improve teaching and learning. Assessing student’s ability and performance with
EDM methods in e-learning environment for math education in school level in India has not been
identified in our literature review. Our method is a novel approach in providing quality math education
with assessments indicating the knowledge level of a student in each lesson. This paper illustrates how
Learning Curve – an EDM visualization method is used to compare rural and urban students’ progress
in learning mathematics in an e-learning environment. The experiment is conducted in two different
schools in Tamil Nadu, India. After practicing the problems the students attended the test and their
interaction data are collected and analyzed their performance in different aspects: Knowledge
component level, time taken to solve a problem, error rate. This work studies the student actions for
identifying learning progress. The results show that the learning curve method is much helpful to the
teachers to visualize the students’ performance in granular level which is not possible manually. Also it
helps the students in knowing about their skill level when they complete each unit.
Dr. S. Saravana Kumar “A Systematic Review on the Educational Data Mining and its Implementation in the Applications ” United International Journal for Research & Technology (UIJRT), Volume 01, Issue 09, pp. 01-03, 2020. https://uijrt.com/articles/v1i9/UIJRTV1I90001.pdf
A Study on Learning Factor Analysis – An Educational Data Mining Technique fo...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Extending the Student’s Performance via K-Means and Blended Learning IJEACS
In this paper, we use the clustering technique to monitor the status of students’ scholastic recital. This paper spotlights on upliftment the education system via K-means clustering. Clustering is the process of grouping the similar objects. Commonly in the academic, the performances of the students are grouped by their Graded Point (GP). We adopted K-means algorithm and implemented it on students’ mark data. This system is a promising index to screen the development of students and categorize the students by their academic performance. From the categories, we train the students based on their GP. It was implemented in MATLAB and obtained the clusters of students exactly.
Cognitive Computing and Education and Learningijtsrd
Its enormous potential in learning spurs Cognitive Computing. The overreaching purpose here is to devise computational frameworks to help us learn better by exploiting the learning process and activities. The research challenge recognized the broad spectrum of human learning, the complex and not fully understood human learning process, and various learning factors, such as pedagogy, technology, and social elements. From the theoretical point of view, Cognitive Computing could replace existing calculators in many applications. This paper focuses on applying data mining and learning analytics, clustering student modeling, and predicting student performance when involved in the education field with possible approaches. Latifa Rahman "Cognitive Computing and Education and Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49783.pdf Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/education/49783/cognitive-computing-and-education-and-learning/latifa-rahman
A Survey on Educational Data Mining TechniquesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement
Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. In this paper we discuss the methodology of assessing the student performance in terms of the success indicators revealing a number of indicators that is recommended to indicate success of the final academic achievement.
Fuzzy Association Rule Mining based Model to Predict Students’ Performance IJECEIAES
The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student’s performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student’s previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The main objective of this paper is to develop a basic prototype model which can determine and extract
unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
from a huge set of data. Data mining techniques are helpful for decision making as well as for discovering patterns
of data. In this paper students eligibility prediction system using Rule based classification is proposed to predict
the eligibility of students based on their details with high prediction accuracy. In Educational Institutes, a
tremendous amount of data is generated. This paper outlines the idea of predicting a particular student’s placement
eligibility by performing operations on the data stored. In this paper an efficient algorithm with the technique
Fuzzy for prediction is proposed.
E-SUPPORTING PERFORMANCE STYLES BASED ON LEARNING ANALYTICS FOR DEVELOPMENT O...IJITE
This study aims to identify the effectiveness of delivering electronic supporting performance styles that are
based on learning analytics for the development of teaching practices in teaching science, moreover, the
Electronic and face to face supporting performance styles will deliver according to the data analytics that
extracted from observations, (participating rate- page views) data from platform, therefore, to determine
the effectiveness, the researchers design observation rubric based on teaching practices standard that
extract from (ASTE/NSTA, AITSL) to observe teaching practices of student science teachers. Regarding the
participants they were science students who enrolled in educational diplomas, researchers use the mixed
method in collected data and quantitative data, furthermore, they will study a supportive program of
considering data analyses to develop their teaching practices in teaching science, the results exposed that
providing a supporting program that considers learning analytics, helps increase teaching practices in
teaching science for student's science teachers.
E-supporting Performance Styles based on Learning Analytics for Development o...IJITE
This study aims to identify the effectiveness of delivering electronic supporting performance styles that are
based on learning analytics for the development of teaching practices in teaching science, moreover, the
Electronic and face to face supporting performance styles will deliver according to the data analytics that
extracted from observations, (participating rate- page views) data from platform, therefore, to determine
the effectiveness, the researchers design observation rubric based on teaching practices standard that
extract from (ASTE/NSTA, AITSL) to observe teaching practices of student science teachers. Regarding the
participants they were science students who enrolled in educational diplomas, researchers use the mixed
method in collected data and quantitative data, furthermore, they will study a supportive program of
considering data analyses to develop their teaching practices in teaching science, the results exposed that
providing a supporting program that considers learning analytics, helps increase teaching practices in
teaching science for student's science teachers.
E-SUPPORTING PERFORMANCE STYLES BASED ON LEARNING ANALYTICS FOR DEVELOPMENT O...IJITE
This study aims to identify the effectiveness of delivering electronic supporting performance styles that are
based on learning analytics for the development of teaching practices in teaching science, moreover, the
Electronic and face to face supporting performance styles will deliver according to the data analytics that
extracted from observations, (participating rate- page views) data from platform, therefore, to determine
the effectiveness, the researchers design observation rubric based on teaching practices standard that
extract from (ASTE/NSTA, AITSL) to observe teaching practices of student science teachers. Regarding the
participants they were science students who enrolled in educational diplomas, researchers use the mixed
method in collected data and quantitative data, furthermore, they will study a supportive program of
considering data analyses to develop their teaching practices in teaching science, the results exposed that
providing a supporting program that considers learning analytics, helps increase teaching practices in
teaching science for student's science teachers.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
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Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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Bob Boule
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SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
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1. ISSN: 2278 - 1323
International Journal of Advanced Research in Computer Engineering and Technology (IJARCET)
Volume 2, Issue 6, June 2013
www.ijarcet.org
2034
Abstract— Educational Data Mining (EDM) is an emerging
trend, concerned with developing methods for exploring the
large data related to educational system. The data is used to
obtain the Implicit and Explicit knowledge distribution.
Educational Data mining methods are generally used to
determine the performance of students, assessment of students
and study students’ behavior etc. In recent years, Educational
data mining has proven to be more successful at many of the
educational statistics problems due to enormous computing
power and data mining algorithms [1]. This research explores
the applications of data mining techniques applied in
educational field.
The work explores SSTS, a new teaching learning process
framework in higher education. The framework contains
students database consists of various relational attributes. By
applying data mining techniques to the database and mined the
effective students’ knowledge distribution clusters using social
network analysis. The analysis found that good student faculty
relation network clusters and average percentile of effective
knowledge distribution by students’ in higher education.
INDEX TERMS— EDUCATIONAL DATA MINING, INTERACTION,
RELATIONSHIP. SSTS FRAMEWORK, TUTOR.
I INTRODUCTION
Educational Data mining (EDM)
EDM develops methods and applies techniques from
statistics, machine learning, and data mining to analyze data
collected during teaching and learning. EDM tests learning
theories and informs educational practice. Learning analytics
applies techniques from information science, sociology,
psychology, statistics, machine learning, and data mining to
analyze data collected during education administration and
services, teaching, and learning. Learning analytics creates
applications that directly influence educational practice
[2][7]. Educational data mining researchers (e.g., Baker
2011; Baker
Manuscript received June, 2013.
M.Sindhuja Computer Science and Engineering, Sri Shanmugha College
of Engineering and TechnologySelam, Tamil Nadu.
Dr.S.Rajalakshmi, Computer Science and Engineering, Jay Shriram group
of Institutions , Tirupur, Tamil Nadu.
S.T.Tharani, Computer Science and Engineering, Jay Shriram group of
Institutions, Tirupur, Tamil Nadu.
and Yacef 2009) view the following as the goals for their
research: Predicting students’ future learning behavior ,
Discovering or improving domain models that characterize
the content to be learned and optimal instructional sequences,
Studying the effects of different kinds of pedagogical support
that can be provided by learning software, Advancing
scientific knowledge about learning and learners through
building computational models that incorporate models of the
student, the domain, and the software’s pedagogy [8]
[6][9][10] . To obtain the above goals, the various techniques
such as prediction, clustering, Relationship mining etc are
used.
II SSTS FRAMEWORK
The teaching learning process is a process of cognitive
thinking. The performance of students is based on their
attitude and relation with their teaching faculty members. Of
course it also includes the teaching methods or aids to
improve the students’ performance. Fig. 1 illustrates a
framework which helps the students’ to progress their
attitude and in academic performance. It relates Staff –
student (SS), student – Tutor (ST) and Tutor – Staff (TS) in
higher education.
Fig. 1. Relation between Staff, Students and Tutor
CLUSTER ANALYSIS OF SSTS
FRAMEWORK USING SOCIAL
NETWORK ANALYSIS
M.Sindhuja, Dr.S.Rajalakshmi, S.T.Tharani
2. ISSN: 2278 - 1323
International Journal of Advanced Research in Computer Engineering and Technology (IJARCET)
Volume 2, Issue 6, June 2013
2035
www.ijarcet.org
III SOCIAL NETWORK ANALYSIS
Social network analysis [11] [5] [4] is the mapping and
measuring of relationships and flows between people,
groups, organizations, computers, URLs, and other
connected information/knowledge entities. The nodes in the
network are the people and groups while the links show
relationships or flows between the nodes. SNA provides both
a visual and a mathematical analysis of human relationships.
a. Experiment
The students’ database have prepared based on general
attributes such as name, age, year and cognitive attributes
base on attitude, behavior and knowledge distribution. It also
includes the SSTS framework attributes SSR, STR and TSR.
The framework is designed in a two way relation.
The students from the dataset are randomly mined by
k-means clustering using TANAGRA irrespective of their
year of studies. A group of 9 students from the dataset were
grouped into a cluster and each group is allocates to a tutor of
the department. Now a day, the faculty members use a good
teaching pedagogy to make the students to understand their
subjects. Learning process involves the knowledge sharing,
intelligence and utilization of resources and relationships.
Fig. 2. Social Network
Fig. 2. shows the social network formed by the whole
students’ dataset with the related attributes.
The work explored that the tutor plays an important role in
the improvement of students’ performance. The clusters
analysis showed the good interaction with the tutor –
students’ group and tutor – staff. The tutor – subject staff and
student – student interaction in the group leaded to take
special care both personal and in academics related. This
explored good academic results and identified it one of the
better framework for teaching learning process in higher
education.
This experiment is done by using social network analysis
software UCINET version 6. The grouped tutor clusters are
given as input and changed to UCINET matrix dataset. It is
then given as input to the DL compiler to link with the
interface. The output is found, and converted into cluster.
Fig. 3. described the cluster of tutor1.
Fig. 3. Tutor1 cluster
The blue dot determines the student as an attribute. The
arrow determines the interaction between the each student.
The cluster is strengthened by the tutor involvement.
Fig. 4. shows the cluster of tutor2.
Fig. 4. Tutor2 cluster
Fig. 5. and Fig. 6. following dendogram describes the
cluster of tutor1 and tutor2 relation.
Fig. 5. Dendogram of tutor1
3. ISSN: 2278 - 1323
International Journal of Advanced Research in Computer Engineering and Technology (IJARCET)
Volume 2, Issue 6, June 2013
www.ijarcet.org
2036
Fig. 6. Dendogram of tutor2
Fig. 7. show the adequate multilevel clusters formed by the
tutor1. This revealed that there is a healthy interaction with
that group of max 3.
Fig. 7. Multilevel Clusters
The proposed work extended the clustering in means of
students’ behavior, attitude of and interactions.
IV CONCLUSION
The research work explored a new SSTS framework in
teaching learning process. The students’ databases contain
information about the related attributes. The matrix data are
mined by using K means clustering from the large dataset
using TANAGRA. The clusters are grouped under tutor and
groups into matrix clusters. The experiment is carried out by
using Social Network Analysis (SNA) tool. The clusters are
analyzed and found fair interactions with the group and staff.
It resulted in improvement of students’ performance in
academics of higher education.
REFERENCES
[1] Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance
metric learning, with application to clustering with
side-information. In: Advances in Neural Information
Processing Systems, vol. 15, pp. 505–512. MIT Press,
Cambridge (2003).
[2] Kosala, R., Blockeel, H.: Web mining research: A survey.
SIDKDD Explorations (July 2000)
[3] Enhancing Teaching and Learning through Educational
Data Mining and Learning Analytics: An Issue Brief,
U.S. Department of Education Office of Educational
Technology, October 2012.
[4] Kwanghoon Kim, “A Workflow-based Social Network
Discovery and Analysis System,” Proceedings of the
International Symposium on Datadriven Process
Discovery and Analysis, Campione d’Italia, ITALY,
June 29-July 1, pp. 163-176, 2011.
[5] E. Ferneley, R. Helms, “Editorial of the Special Issue on
Social Networking,” Journal of Information
Technology, Vol.25, No.2, pp. 107- 108, 2010.
[6] Educational Data mining as a Trend of Data Mining in
Educational System, Sachin 7. R. Barahate,
International Conference & Workshop on Recent Trends
in Technology, (TCET) 2012 Proceedings
published in International Journal of Computer, pp.
11-16.
[7] Barnes T., “The q-matrix method: Mining student
response data for knowledge”, In Proceedings of the
AAAI-2005 Workshop on Educational Data Mining,
2005.
[8] Brijesh Kumar Baradwaj, Saurabh Pal, “Mining
Educational Data to Analyze Students’ Performance”, In
International Journal of Advanced Computer Science and
Applications, Vol. 2, No. 6, pp. 63-69, 2011.
[9] Castro F., Vellido A., Nebot A., & Minguillon J.,
“Detecting atypical student behavior on an e-learning
system”, In I Simposio Nacional de Tecnologas de la
Informacin y las Comunicaciones en la Educacin,
Granada, pp. 153–160, 2005.
[10] Romero C., & Ventura S., “Educational data mining: A
survey from 1995 to 2005”, Expert Systems with
Applications, 33(1), pp. 135–146, 2007.
[11] Scott J., “Social network analysis: A handbook”, (2nd
ed.). Newberry Park, CA: Sage, 2000.
M.Sindhuja, received her B.E degree in Computer Science and
Engineering and M.E degree in Computer Science and Engineering. Now
working as Assistant professor in Sri Shanmugha College of Engineering and
Technology.
Dr.S.Rajalakshmi received her B.E degree in Computer Science and
Engineering from Mahendra Engineering College in 2003, M.E degree in
Computer Science and Engineering from Muthayammal Engineering
College in 2007 and Ph.D in Computer Science and Engineering at Anna
University Chennai in 2012. Her area of Interest is Knowledge Engineering,
Data Mining, Artificial Intelligence & Sensor Networks. She has 9 years of
teaching experience and 5 years in Research.
S.T.Tharani received her B.E Computer science and Engineering from
Anna University, Coimbatore, M.E degree in Computer Science and
Engineering from Avinasilingam University. Now working as Assistant
Professor in Jay Shriram Group of Institutions.