20151112PosterJanHellingsV2_definitief
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Many students failed an introductory Java programming course. A study developed dashboards displaying students' online activity and predicted performance to provide weekly feedback. Students receiving dashboards completed more online tasks but did not have significantly higher pass rates or exam scores compared to the control group. The aim was to increase course success through personalized feedback on e-learning progress.
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This document describes a study that uses machine learning models to predict student performance and whether students will complete their degrees based on their academic records and other features. The study collected data on scholarship students from various universities. It applied learning analytics, discriminative, and generative classification models to the data. Experimental results showed the proposed method, which considered features like family expenditures and personal information, outperformed existing methods that primarily used academic performance, family income, and assets. The document discusses using k-means clustering and support vector machines (SVM) algorithms to analyze the data and predict student performance. It concludes that past academic performance significantly influences students' future performance and that predictive performance increases with larger datasets.
B05110409
The document proposes a new framework called Quasi Framework to detect disengagement in online learning. It analyzes log file data from an online learning system to identify attributes related to disengagement. The framework merges log file information with student database information and uses it to predict disengagement. Experimental results on a real student dataset show the Quasi Framework achieves higher accuracy than an existing system called iHelp, particularly for predicting disengaged students. The study suggests considering both reading and assessment attributes are important for accurate disengagement detection.
Investigating learning strategies in a dispositional learning analytics conte...
This document discusses a study analyzing how students use worked examples, tutored problem-solving, and untutored problem-solving in an online math tutorial system called SOWISO. The study examines how these learning modes relate to student performance and dispositions. Key findings include: (1) engagement with tutorials and mastery of content strongly predicts exam scores; (2) students frequently use worked examples and rarely use hints for tutored problem-solving; (3) adaptive dispositions correlate with timely preparation and less example use, while maladaptive dispositions correlate with less preparation and more example use.
Data Mining Techniques for School Failure and Dropout System
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:– ...
Student academic performance is the great value of institutes, universities and colleges. All colleges majorly focus on the career development of students. The academic performance of students plays a vital role in the establishment of a bright career. On the basis of better academic performance, the placement of the students will be better and the same will be reflected in the form of better admission and future. Machine learning can be deployed for the prediction of student performance. Various algorithms are playing an important role in the prediction of the accuracy of various machine learning models. These articles discuss various algorithms that can be helpful to deploy for predicting student academic performance. The article discusses various methods, predictive features and the accuracy of machine learning algorithms. The primary factors used for predicting students performance are academic institution, sessional marks, semester progress, family occupation, methods and algorithms. The accuracy level of various machine learning algorithms is discussed in this article.
Educational Data Mining to Analyze Students Performance – Concept Plan
This document discusses using data mining techniques to analyze student performance data from educational institutions. It proposes using clustering and classification algorithms like K-means and Naive Bayesian on data collected from sources like learning management systems and surveys. The goals are to classify students into performance levels, identify factors affecting performance, and make recommendations to help students improve. Clustering could group students and classification could predict performance based on attributes. Analyzing the data may provide insights to enhance guidance and outcomes. The paper presents this as a conceptual plan to apply data mining in education.
C04622028
The document describes the design and evaluation of an electronic class record system for Makiling National High School. It discusses:
1) The development of an electronic class record using Microsoft Excel that automatically computes student grades based on inputs from teachers.
2) Testing the functionality and accuracy of the electronic class record system.
3) Evaluating the acceptability of the electronic class record system through surveys of teachers, finding it was rated positively and would be implemented in the upcoming school year.
Intelligent system for sTudent placement
This document describes a proposed intelligent system for student placement in Nigeria using fuzzy logic. It outlines the country's educational system and related works on student placement prediction. The proposed system would use fuzzy logic and linguistic variables to analyze student data like academic performance, psychomotor skills, and department choices. Membership functions would be assigned to variables and an inference engine would apply fuzzy rules to generate placement recommendations to either the science, arts, or repeat a class. The goal is to help schools accurately place students in a timely manner to improve performance and outcomes.
IRJET- Using Data Mining to Predict Students Performance
This document describes a study that used logistic regression to predict student performance based on educational data. The researchers collected student data including exam scores, attendance, study hours, family income, etc. from a large dataset. Logistic regression achieved the best prediction accuracy of 82.03% compared to other models like naive bayes, K-nearest neighbor, and multi-layer perceptron. The results indicate that around 230 students would perform poorly, 600 would perform fairly, and 200 would perform well based on the predictive model. This analysis can help identify students needing extra support and help universities improve academic outcomes.
An Artificial Intelligence-Based Distance Education System Artimat
The document describes an artificial intelligence-based distance education system called ARTIMAT. It was developed to improve students' mathematical problem solving skills. The system was tested with 4 teachers and 59 students. Survey results found that the system was generally successful in helping students learn problem solving concepts and processes, but some interface changes were needed for students to adapt quickly. The system uses artificial intelligence techniques like forward and backward chaining to analyze problems and break them into sub-problems to guide students through the solution process step-by-step.
Effects of e instruction in Teaching Intro to IT
A study conducted to determine the effects of using e-Instruction in Teaching IT Concepts to non-IT Courses at Arellano University
M-Learners Performance Using Intelligence and Adaptive E-Learning Classify th...
This document discusses using machine learning classification algorithms to predict student performance based on educational data. It compares the performance of five classification algorithms - J48, Naive Bayes, Bayes Net, Backpropagation Network, and Radial Basis Function Network - in predicting student academic achievement using attributes like demographic information, test scores, and academic factors. The experiment found that the Radial Basis Function Network algorithm achieved the highest accuracy, correctly classifying 100% of instances, compared to 75-95% accuracy for the other algorithms. Convolutional neural networks are also discussed as a powerful tool for image and language processing in educational data mining.
An Expert System For Improving Web-Based Problem-Solving Ability Of Students
The document describes an expert system developed to improve students' web-based problem solving abilities. It analyzes the online problem solving behaviors of teachers to build the knowledge base. Quantitative indicators are used to describe teachers' web searching behaviors, which are then categorized and analyzed using factor analysis. Experimental results showed the expert system was able to provide accurate suggestions to students for improving their problem solving skills.
Student Performance Evaluation in Education Sector Using Prediction and Clust...
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Fuzzy Association Rule Mining based Model to Predict Students’ Performance
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.
EDM_IJTIR_Article_201504020
This document summarizes a research paper that evaluates the performance of decision tree and clustering techniques using the WEKA data mining tool. The paper uses student academic and performance data to apply decision tree and clustering algorithms and compare the results of each technique. Specifically, it uses WEKA to classify and cluster a dataset containing the marks and percentages of students from educational institutions. The paper aims to determine which technique (decision tree or clustering) provides more accurate and useful results for predicting student performance.
A Survey on Research work in Educational Data Mining
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.
G017224349
This document summarizes research on educational data mining. It discusses topics such as student modeling, improving educational software, mining assessment data, and generic frameworks/methods. Student modeling research focuses on automatically improving student models and predicting student performance. Research on improving software examines identifying learning behaviors and adapting intelligent tutoring systems based on individual differences. Assessment data mining analyzes optimal/worst-case mastery learning and predicting dropout using social behavior data. Generic frameworks include knowledge tracing approaches and tools for visualizing interaction networks. The conclusion recommends continued collaboration across research, education, and industry to further the field.
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IRJET- Evaluation Technique of Student Performance in various Courses
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Investigating learning strategies in a dispositional learning analytics conte...
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Data Mining Techniques for School Failure and Dropout System
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ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:– ...
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Educational Data Mining to Analyze Students Performance – Concept Plan
Educational Data Mining to Analyze Students Performance – Concept Plan
IRJET- Using Data Mining to Predict Students Performance
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An Artificial Intelligence-Based Distance Education System Artimat
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M-Learners Performance Using Intelligence and Adaptive E-Learning Classify th...
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An Expert System For Improving Web-Based Problem-Solving Ability Of Students
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Fuzzy Association Rule Mining based Model to Predict Students’ Performance
Fuzzy Association Rule Mining based Model to Predict Students’ Performance
A Survey on Research work in Educational Data Mining
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20151112PosterJanHellingsV2_definitief
- 1. Learning analytics dashboard for a Programming course 0,0 % 10,0 % 20,0 % 40,0 % 50,0 % 30,0 % Failed <5.5 Passed <5.5 <9.0 Well passed <9.0 <=10.0 Percent Result programming N=1217 Group statistics Cohort n= 684 Online yes/no N Mean Std. Deviation Std. Error Mean 2014 Result 0 453 4,785 3,4785 ,1634 1 231 6,833 2,6778 ,1762 • Many freshman computer science students failed the essential 10 week Java programming course (42%) (figure 1). • This course is supported by e-learning systems. • Students who performed all their task in the e-learning system were more successful. (table 1) Figure 1: Result of Java programming course N=1217 Table 1: Difference in result programming Yes/no all online tasks. (t(682)= -7,84 p <.000) Developing a dashboard application for giving feedback on tasks in e- learning systems. The aim is to increase the amount of students passing by giving feedback on their online behavior. • The activities of the students in e-learning environments tend to be pre- dictable for results on the course (Hu, Lo, & Shih, 2014; Tempelaar, Rien- ties, & Giesbers, 2014) • Courses using early alert systems for at-risk students have a 6% higher passing rate (Jayaprakash, Moody, Eitel, Regan, & Baron, 2014) • User interface of dashboard is important. Graphical elements give more user satisfaction (Ali, Asadi, Gašević, Jovanović, & Hatala, 2013) • Computer-assisted instructional feedback has an effect size of 0,52 and feedback on the task level is effective (John Hattie & Timperley, 2007) The dashboard (figure 2) displays the progress in the e-learning systems, the expected result and com- pared this to the performance of the total cohort (n=556). Figure 2: Dashboard of student week 1 In Figure 3 the creation of the 8 dashboards is visualized. Every week the dashboards of the total cohort (n=556) were generated. • Moodle: online data; quiz and practical assignment results. • MyProgrammingLab: Mastery score and total attempts. • For the expected result, linear regression models are used, and for the risk of failure decision tree al- gorithms (Decision Stump, Adaboost)(Hu et al., 2014) are used. These models were created through WEKA 3.6. Figure 3: Visualization of Dashboard generation • The experiment was set up like a RCT (figure 4) (Randomized Controlled Trial) (Murnane & Willett, 2010). • Opt-out: Students could chose for an opt-out. Only two students chose for an opt-out. • 556 students were incorporated in the experiment. • The students were conditionally randomized. Every week 276 students in the treatment group re- ceived an e-mail with a personalized link to their dashboard. • The effect of the use of the dashboard is determined by the difference in the exam results of the treatment group and the control group (figure 4). Figure 4: Setup of the randomized controlled experiment • Q1: Do students receiving a dashboard have a higher passing rate? • Q2: Do students receiving a dashboard have a better result? • Q3: Do students receiving a dashboard make more use of the e-learning systems? 1. Tempelaar, D. T., Rienties, B., & Giesbers, B. (2014). In search for the most informative data for feedback generation: Learning analytics in a data-rich con- text. Computers in Human Behavior. http://doi.org/10.1016/j.chb.2014.05.038 2. Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36(0), 469–478. http://doi.org/http://dx.doi.org/10.1016/j.chb.2014.04.002 3. Jayaprakash, S. M., Moody, E. W., Eitel, J. M., Regan, J. R., & Baron, J. D. (2014). Early Alert of Academically At-Risk Students : An Open Source Analytics Ini- tiative. Journal of Learning Analytics, 1, 6–47. 4. Murnane, R. J., & Willett, J. B. (2010). Methods matter: Improving causal inference in educational and social science research. Oxford University Press. Introduction Objectives Related research Dashboard Method Research questions Test scores References Table 2: Difference in passing rate control vs. treatment group (X2 = ,319a p= ,572) First Year computer science student Objection against experiment Takes part of the experiment Doesn’͛ t take part of experiment Conditionaly randomize Assign to treatment group Assign to control group No Yes Result programming Result programming end Receives every week a dashboard ˄ Result of intervention 0 50 100 200 150 No dashboard Dashboard Count Bar Chart Passed Failed Passed Participation Improve the success rate of a Java programming by giving online feedback on the progress of e-learning tasks Figure 5: Result exam passing rate of control and treatment group Passed Total Failed Passed Participation No dashboard Count 162 118 280 % within participation 57,9% 42,1% 100% Dashboard Count 165 109 274 % within participation 60,2% 39,8% 100% Total Count 327 227 554 % within participation 59,0% 41,0% 100% Week 0 Week1 Dashboard 1 Demographic data Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 1 week2 Dashboard 2 Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 2 week3 Dashboard 3 Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 3 week4 Dashboard 4 Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 4 week5 Dashboard 5 Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 5 week6 Dashboard 6 Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 6 week7 Dashboard 7 Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 6 week8 Dashboard 8 Myproglab #Attemps Mastery Moodle Quiz result Pracitcal Assignme nt Predic tion Model 8 Jan Hellings. E-mail: j.f.hellings@hva.nl