Usage reporting on recorded lectures using educational data mining

Gorissen, Pierre; Bruggen, Jan Van; Jochems, Wmg Wim

doi:10.1504/ijlt.2012.046864

Cited by 29 publications

(30 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysis of video viewing data is a relatively recent trend and there is a small but increasing body of on-going research that focuses on the topic (e.g. Li et al 2015a, 2015b, Giannakos et al 2015, Giannakos et al 2014b, Kleftodimos & Evangelidis 2014a, b, Brooks et al 2013, Gorissen et al 2012, De Boer et al 2011. Thus, it is certainly a plus for a tool to provide more advanced analytics features but most importantly to provide the whole dataset of viewing and activity data that will enable researchers to conduct more elaborate data analysis by applying advanced statistical methods, visualizations and data mining algorithms.…”

Section: Introductionmentioning

confidence: 99%

Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics

Kleftodimos

Evangelidis

2016

Smart Learn. Environ.

View full text Add to dashboard Cite

The use of online videos is a common practice today amongst education professionals. Interactive features in videos are constantly evolving and a recent trend is the integration of interactive elements and web content into educational videos. The paper (a) provides a roadmap for using open source tools and open internet resources to develop a learning environment where video content is aggregated with interactive elements, educator content and content coming from the web, (b) describes how these open source tools are used for capturing and storing learner activity data, and, (c) presents findings obtained from analyzing learner activity data gathered in an educational setting during an academic year.

show abstract

Section: Introductionmentioning

confidence: 99%

Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics

Kleftodimos

Evangelidis

2016

Smart Learn. Environ.

View full text Add to dashboard Cite

show abstract

“…In recent years, there is a growing interest in employing educational data mining techniques (EDM) to conduct the automatic analysis of learner performance and behavioral data in learning environments. Various experiments have been carried out in the EDM area that assess student learning performance, improve the learning process, guide student learning, provide feedback, adapt learning recommendations based on student learning behaviors, and evaluate learning materials and courseware [1,11,7]. An emerging trend in EDM is the use of text mining which is an extension of data mining to text data.…”

Section: Related Workmentioning

confidence: 99%

Correlation of Topic Model and Student Grades Using Comment Data Mining

Sorour

Goda

Mine

2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

View full text Add to dashboard Cite

Assessment of learning progress and learning gain play a pivotal role in education fields. New technologies like comment data mining promote the use of new types of contents; student comments highly reflect student learning attitudes and activities compared to more traditional methods and they can be a powerful source of data for all forms of assessment. A teacher just asks students after every lesson to freely describe and write about their learning situations and behaviors. This paper proposes new methods based on a statistical latent class "Topics" for the task of student grade prediction; our methods convert student comments using latent semantic analysis (LSA) and probabilistic latent semantic analysis (PLSA), and generate prediction models using support vector machine (SVM) and artificial neural network (ANN) to predict student final grades. The experimental results show that our methods can accurately predict student grades based on comment data.

show abstract

“…They found that students who used the hinting system achieved significantly better results than those who did not, and stayed active on the site longer. Gorissen et al [51] analyzed the interactions of students with the recorded lectures using educational data mining techniques. They found discrepancies as well as similarities between students' verbal reports and actual usage as logged by the recorded lecture servers.…”

Section: Edm Applications For Predicting and Evaluatingmentioning

confidence: 99%

“…However, some studies also used interaction traces [32,48], communication traces [28,56], and responses to satisfaction questionnaire [50] or combined between several types of data such as in [23,44,48,51]. Another approach that we found in [31] was to generate simulated data using a probability matrix in order to test several models.…”

Section: Edm Applications For Predicting and Evaluatingmentioning

confidence: 99%

“…We also notice that the data set size is very variable: from 27 [56] to 4,927 [51] participants producing data over several hours, weeks, months or years attending a big data set size (over 20 millions in [54]). This is related to the context of the study and the data origin: data collected in experiments or dataset already available and used in previous experiments (e.g.…”

Section: Edm Applications For Predicting and Evaluatingmentioning

confidence: 99%

See 1 more Smart Citation

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

Bousbia

Belamri

2013

Educational Data Mining

View full text Add to dashboard Cite

Educational Data Mining is a new growing research area that can be defined as the application of data mining techniques on raw data from educational systems in order to respond to the educational questions and problems, and also to discover the information hidden after this data. Over the last few years, the popularity of this field enhanced a large number of research studies that is difficult to surround and to identify the contribution of data mining techniques in educational systems. In fact, exploit and understand the raw data collected from educational systems can be ''a gold mine'' to help the designers and the users of these systems improving their performance and extracting useful information on the behaviors of students in the learning process. The use of data mining techniques in e-learning systems could be very interesting to resolve learning problems. Researchers' ambition is to respond to questions like: What can predict learners' success? Which scenario sequence is more efficient for a specific student? What are the student actions that indicate the learning progress? What are the characteristics of a learning environment allowing a better learning? etc. The current feedback allows detecting the usefulness of applying EDM on visualizing and describing the learning raw data. The predictions take also an interest, particularly the prediction of performance and learners' behaviors. The aim of this chapter is to establish a bibliographic review of the various studies made in the field of educational data mining (EDM) to identify the different aspects studied: the analyzed data, the objectives of these studies, the used techniques and the contribution of the application of these techniques in the field of computer based learning. The goal is not only to list the existing work but also to facilitate the use and the understanding of data mining techniques to help the educational field specialists to give their

show abstract

Usage reporting on recorded lectures using educational data mining

Cited by 29 publications

References 12 publications

Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics

Using open source technologies and open internet resources for building an interactive video based learning environment that supports learning analytics

Correlation of Topic Model and Student Grades Using Comment Data Mining

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

Contact Info

Product

Resources

About