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Patterns and pattern languages are mechanisms to describe best practices and good designs to capture experience in a way that it is possible for others to reuse this experience. Similarly, pedagogical patterns try to capture expert knowledge regarding the practice of teaching and learning. Issues related to teaching and learning have been increasingly discussed and studied, particularly computational...
The overall goal of our Software Engineering Teamwork Assessment and Prediction (SETAP) project is to develop effective machine-learning-based methods for assessment and early prediction of student learning effectiveness in software engineering teamwork. Specifically, we use the Random Forest (RF) machine learning (ML) method to predict the effectiveness of software engineering teamwork learning based...
Today, the use of learning analytics is becoming more crucial in the learning environment for the purpose of understanding and optimizing students' learning situations. The purpose of this paper is to examine the impacts of Teacher Interventions (TIs) on students' attitudes and achievements involved with the lesson by analyzing their freestyle comment data after every lesson. The current study proposes...
It is important for students to solve problems with specific requirements in the programming teaching. Our teaching system is a Moodle-based interactive teaching platform for C programming. Its online judging system can grade students code automatically. It plays an extremely important role in programming language teaching. This paper is devoted to optimizing and improving the system. We firstly analyze...
Educational data is in abundance due the nature of education in the 21st century where a significant amount of education occurs online through the advent of digital learning. There is an increase in the use of Learning Management Systems (LMS) to provide education without boundaries, and these systems hold large amounts of data. Educational data mining is the process of tapping into this rich educational...
Educational Data mining is a varied subject and unwary research field which are supposed taken care of the tremendous data which are arising in the educational field. The exploring data is formed as a data set and then various computational techniques are applied to derive an interesting pattern out of it with which further analysis, decisions can be done for the re-modeling of learning pattern, enhancement...
Analysis of course survey data are foundationally important because it has a close relation with students' skills training and talent development. However, there are multiple categories of classification information that characterized different students from different perspectives, such as genders, classes, types or regions. Compared to the correlation between survey data with individual kind of classification,...
Due to the growing interest in data mining and the educational system, educational data mining is the emerging topic for research community. The various techniques of data mining like classification and clustering can be applied to bring out hidden knowledge from the educational data. Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches...
The propose of this research was to classify the English as a foreign language (EFL) learners based on their performance on the reading test. Three levels of reading comprehension are customarily defined: (1) Factual level or Reading the lines, (2) Interpretive level or Reading between the lines, and (3) Evaluative level or Reading beyond the lines. Further analyzing and synthesizing factors underlying...
With the increasing diversity of learners, forming suitable learning groups, in collaborative learning, represents a complex and a time-consuming task. Several researchers focus on the theory of teamwork organizations, based on academic performances, learning styles, learning settings, gender, etc. Therefore, grouping learners based on their predicted academic performance level in each subject separately...
This paper is part of a doctoral thesis that aims to propose an evaluation model, for later application, using Educational Data Mining techniques to analyze the responses of students obtained during an Institutional Teaching Evaluation. Therefore, the authors propose an Institutional Teaching Evaluation model that applies, among others, the Sentiment Analysis to identify which teaching practices are...
The usability of comparable technology in the higher education is very important for supporting knowledge management at industry assisted technology. An Information Technology (IT) platform with administrators, instructors, and students is necessary to achieve a powerful learning content. This process requires an educational lifeloop management approach. This approach provides some features such as...
Through the processes set out by Learning Analytics, this paper describes a decision making model which supports the managers of higher education institutions at the moment of making academic decisions. In the analysis and requirements phase, the importance of creating and categorizing indicators is presented in detail, thereby facilitating the finding of hidden patterns in the educational information...
This paper discusses part of the main work in field of data science, mining and analytics. Family of algorithms is developed to predict the educational relevance of individuals' talents through lens of personality features (unstructured and semi-structured) and academic/career data. This paper presents progress of results in Good Fit Students (GFS) algorithms and math construct. This work addresses...
This paper aims to describe the analysis of data from the Moodle's database of a beginner class in Distance Education of a Federal University using distinct educational data mining clustering methods. We carried out clustering using hierarchical and non-hierarchical methods in different groups of students, according to their interaction and performance characteristics. In the analysis, it was possible...
Massive Open Online Course(MOOC) is undergoing explosive growth recently, both the number of MOOC platforms and courses are increasing dramatically during these years. One of the major concerns in MOOC is high dropout rate, we study dropout prediction in MOOCs, using student's learning activities data in a period of time to measure how likely students would drop out in next couple of days. We collect...
How to get the information we need from the existing data, even extract the hidden message, and then transform them into knowledge, is an important skill we must learn in this era. Data mining technology in recent years is increasing attention in various fields, because of its extraordinary process. Usually, when we find more unexpected information, it may have the higher the value. This paper proposed...
In this paper, we use apriori algorithm to find the influence factors of the satisfaction with compulsory education funds. After the rules generated, we eliminate the rules with many antecedent items if their values of Confidence aren't significantly higher than those of the rules with less antecedent items. According to the properties of some commonly objective interestingness measures and the result...
MOOC develops quite well and provides a new learning platform, which becomes a prominent representative of big data due to its massive behavior data accumulated in the process of using vast teaching resources. Big data analytics technology based on MOOC is a new research trend and its frame can be involved in four stages: where to get big data (Where), what are data types of MOOC (What), how to deal...
The traditional analyzing way of ideological and political education is based on the calculation of the absolute points, which has drawback and insufficient in the results objectiveness and veracity. K-means algorithm is introduced in ideological and political education management, which depends on selection of initial center point and determination of optimal clustering number. So, a kind of improved...
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