#educational data mining

[bookpiofficial]

Exploring the High Potential Factors that Affects Students’ Academic Performance: A Recent Study Approach | Chapter  01 | Novel Perspectives of Engineering Research Vol. 8

Because of the increasing growth of the student population, educational facilities at all levels have been expanded. Teachers are now tasked with a multiplicity of duties. Teachers are responsible for guiding pupils in choosing a career path based on their strengths and aptitudes. Data Mining is the method of extracting educational data from enormous amounts of data in order to improve the quality of educational activities. Individuals' problem-solving and decision-making skills, as well as their social skills, must be developed in today's educational system. Educational Data Mining is one of the Data Mining applications used in educational institutions to find hidden patterns and information. Three key student groups have been identified: fast learners, average learners, and slow learners. In fact, students are likely to have difficulties in a variety of ways. The primary goal of this research is to improve the prediction of students' academic performance by finding significant traits using the attribute selection approach. This research focuses on identifying high-potential elements that influence college students' success. This discovery will have a favourable impact on the students' academic achievement. Author(S) Details R. Kaviyarasi Principal, Sri Vidya Mandir Arts & Science College (A), Uthangarai, Krishnagiri (Dt)- 636902 Tamilnadu, India. T. Balasubramanian Principal, Sri Vidya Mandir Arts & Science College (A), Uthangarai, Krishnagiri (Dt)- 636902 Tamilnadu, India. View Book:- https://stm.bookpi.org/NPER-V8/article/view/6107

[playpowerblogs]

Book Review: Handbook of Educational Data Mining |Playpower Labs

In this short article, I am going to review the Handbook of Educational Data Mining. It is one of the most amazing introductions to the Educational Data Mining field. The book is a collection of articles divided into two parts. The first section walks through data mining methods commonly used in Educational Data Mining (or EDM) field, and the second section is a collection of case studies that presents so many different dimensions and applications of EDM methods. The first part of the book has a

The Handbook of Educational Data Mining is an amazing introduction to the field. EDM methods are becoming relevant as more educators are using educational data every day. The explosion of online learning has given a boost to the EDM field, as platforms collect vast amounts of learner data.

[ijtsrd]

Mining Educational Data to Predict Students' Future Performance using Naïve Bayesian Algorithm

by Nilaraye ""Mining Educational Data to Predict Students' Future Performance using Naïve Bayesian Algorithm""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd26642.pdf  

Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/26642/mining-educational-data-to-predict-students%E2%80%99-future-performance-using-na%C3%AFve-bayesian-algorithm/nilaraye

ugc approved journals for social science, management journal, paper publication for engineering

Higher education institutions want not only to provide quality education to its students but also to advice career options according to the prediction of students' performance. The students' satisfactory performance takes an important role to give birth the best quality graduates who will become competent laborers for the country's economic and social development 2 . Students' performance like who will pass and who are likely to fail can be predicted with the help of lots of features available. The students want to realize their final performance before the announcement of their results and before they attend their semester exams. According to their predicted performance, the students can improve their skills by proper planning to lead to a good performance in their end examination. To provide a good advice to such kind of student, educational data mining system is implemented to predict students' final performance evaluated by considering factors which include IM, PSM, Basics, ACIC, ASS, CP, ATT, ACOC and ESM. In this research, an attempt has been made to explore Naïve Bayesian classification to predict the students' future performance. 

[ijtsrd]

Educational Data Mining A Blend of Heuristic and K-Means Algorithm to Cluster Students to Predict Placement Chance

by Ashok. M. V | G. Hareesh Kumar "Educational Data Mining: A Blend of Heuristic and K-Means Algorithm to Cluster Students to Predict Placement Chance"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018,

URL: http://www.ijtsrd.com/papers/ijtsrd18882.pdf

Direct Link: http://www.ijtsrd.com/computer-science/data-miining/18882/educational-data-mining-a-blend-of-heuristic-and-k-means-algorithm-to-cluster-students-to-predict-placement-chance/ashok-m-v

call for paper international journal, ugc list of journals, best international journal

Educational data mining emphasizes on developing algorithms and new tools for identifying distinctive sorts of data that come from educational settings, to better understand students. The objective of this paper is to cluster efficient students among the students of the educational institution to predict placement chance. Data mining approach used is clustering. Ablend of heuristic and K-means algorithm is employed to cluster students based on KSA knowledge, Communication skill and attitude . To assess the performance of the program, a student data set from an institution in Bangalore were collected for the study as a synthetic knowledge. A model is proposed to obtain the result. The accuracy of the results obtained from the proposed algorithm was found to be promising when compared to other clustering algorithms. 

[matnel]

EdX and breadcrumbs of data

The quest for modulestore.json and other tales of EdX data analytics is a multi-chapter blog story examining my experiences of educational data mining based on a short course I took while at UC Berkeley.

EdX is one of the big MOOC platforms out there, originating from MIT and Harvard. They provide the platform in the clouds but you can download the full code also and run it on your own. The courses are formed in the traditional MOOC way: there are weeks, and per week there are (video) lectures, exercises and assignments, discussion and other materials. The exercises can take several forms, like multi-choices, inputting equations, numbers or text or pointing from the picture, or if you really need something you can develop your own modules too.

The cool part for us, the researchers, is users’ interaction with the site is stored in a huge file. I mean huge, enormous! And the data logged is also rather detailed, for videos the level is checking when transcripts are displayed and when not. Everything is stored in a nice format that can be accessed

{"username": "miller", "host": "class.stanford.edu", "session": "fa715506e8eccc99fddffc6280328c8b", "event_source": "browser", "event_type": "hide_transcript", "time": "2013-07-31T06:27:10.877992+00:00", "ip": "27.7.56.215", "event": "{\"id\":\"i4x-Medicine-HRP258-videoalpha-09839728fc9c48b5b580f17b5b348edd\", \"code\":\"fQ3-TeuyTOY\", \"currentTime\":0}", "agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.72 Safari/537.36", "page": "https://class.stanford.edu/courses/Medicine/HRP258/Statistics_in_Medicine/courseware/495757ee7b25401599b1ef0495b068e4/6fd116e15ab9436fa70b8c22474b3c17/" }

{"username": "jane", "host": "class.stanford.edu", "event_source": "server", "event_type": "/courses/Education/EDUC115N/How_to_Learn_Math/modx/i4x://Education/EDUC115N/combinedopenended/c415227048464571a99c2c430843a4d6/get_results", "time": "2013-07-31T06:27:06.222843+00:00", "ip": "67.166.146.73", "event": "{\"POST\": { \"task_number\": [\"0\"]}, \"GET\": {}}", "agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.71 Safari/537.36", "page": null }

(Examples are documented as part of a parser)

However, to conduct any research on this, we first need to get the correct data out from these files. So try to extract whatever is there and understand what it means: the first example is related to use of transcripts, but the latter one is someone checking the results on a certain exercise. So if we examine interactions with video, the latter is just waste! Luckily we are not alone here. Both Stanford and Harvard have written some scripts to extract the data. On the course, I took more careful look on the Stanford side of the scripts, and first just tried to run the script, however that try was not that successful:

The authenticity of host 'deploy.prod.class.stanford.edu (54.215.215.123)' can't be established.

So it seemed their scripts have interesting dependencies with Stanford servers and we indeed needed to get our hands dirty. I did what I do best: wrote some additional code and solved the problems.

[matnel]

Chapter 1: Education, data and machine learning

The quest for modulestore.json and other tales of EdX data analytics is a multi-chapter blog story examining my experiences of educational data mining based on a short course I took while at UC Berkeley.

One of the most challenging but also most rewarding courses I've taken during my exchange at the University of California, Berkeley was "Info 290: Machine Learning in Education." The idea was to apply machine learning techniques, that is heavy computing and some statistics, to educational data. Our journey started with simulated datasets, but the destiny was clear(ish): apply some of our knowledge to real data from Berkeley’s EdX and gain novel insights into students’ learning. However some of you are left to ponder what kind of insight we may collect to support the idea of learning. There is a long established tradition of knowledge tracing, meaning exploration of students’ responses and based on those, interpreted the likelihood that student indeed knows the question. To simplify the phenomena, we assume there are three parameters that affect how students’ responses change from time to time: the probability that student learned something, the change that student just guessed correctly and the change that student actually knew the correct answer but slipped and answered incorrectly. And yes, this is good for certain questions, not so good for more open ended themes. Such is life.

,

(Figure from Pardos & Heffernan, 2010)

So we have three parameters that describe how people transfer from one answer to the next one, we also have one more parameter: the estimated initial knowledge that the students knew the topic even before any teaching.

Now this is the time to start your clusters! (Clusters are server machines that can be used to do heavy calculation.) A method called Hidden Markov Models (HMM) allows us to conduct this kind of estimation: we try to fit different parameters to the data (and the HMM based on the data) and choose the set of parameters, which fit to the data best. Simple in theory, in practice one-time run like this can take days to come through. To summarize: we take students’ responses and analyze them using a magical calculation and based on that can propose how well the student actually knew the topic. However, to do this kind of stuff, we need data. Luckily UC Berkeley has online courses so let’s go!

[jimharland]

Educational Data Mining

Educational Data Mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in. 

Educational Data Mining focuses on developing new tools and algorithms for discovering data patterns. 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.

Educational data mining is emerging as a research area with a suite of computational and psychological methods and research approaches for understanding how students learn. New computer-supported interactive learning methods and tools - intelligent tutoring systems, simulations, games - have opened up opportunities to collect and analyze student data, to discover patterns and trends in those data, and to make new discoveries and test hypotheses about how students learn. Data collected from online learning systems can be aggregated over large numbers of students and can contain many variables that data mining algorithms can explore for model building.

Goals of EDM:

Predicting students’ future learning behavior by creating student models that incorporate such detailed information as students’ knowledge, motivation, metacognition, and attitudes; 

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; and 

Advancing scientific knowledge about learning and learners through building computational models that incorporate models of the student, the domain, and the software’s pedagogy.

Digging Deep into Educational Data

Educational Data Mining: Predict the Future, Change the Future