Data Web house to support Web intelligence in e-learning environments

Zorrilla, Marta E.; Millán, Socorro; Menasalvas, Ernestina

doi:10.1109/grc.2005.1547386

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…Most often than not, assistance from specialized tools is needed to extract useful information for tracking and assessing the activities performed by students, especially in cases where the faculty member is handling multiple classes. At the same time, although the Moodle system provides some reporting tools, it does not provide specific features which can enable educators to directly monitor and evaluate the activities of students in relation to the structure and contents of the course and how it affects the learning process (Zorrilla, Millan, & Menasalvas, 2005). Based on experience, this keeps instructors from making meaningful sense and use of this data (Estacio & Raga, 2017 This paper describes a pilot study that discusses and illustrates the use of a novel approach in analyzing the log data generated by Moodle in a blended learning context.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Raga

Raga²

2017

IJCSR

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Purpose -This paper proposes a novel approach for processing course log data obtained from Moodle-based blended courses in order to visualize patterns of student activity within the online environment and to determine whether these log data can be used to predict student academic performance.Method -Logs of student activities were summarized and processed using the Vector Space Model approach. This resulted in a novel vector-based form of representation which can be used to map students' activity in a latent activity space given a set of activity dimensions. An enriched form of this representation was also generated by processing the DateTime and IP address metadata for the purpose of developing classification/predictive model of students' performance. 2Results -The activity space coupled with a one-hot vector representation for each unique activity dimension can be used to visualize the differences in level and type of activity preferences of students. Experiments using several machine learning algorithms indicate that the generated model can modestly distinguish between sets of activities that lead to High, Low, or Failed performances.Conclusion -The development of easily interpretable graphics that can depict trends in student activity is a useful tool for instructors handling blended courses. It can provide constant monitoring of course progression with minimal effort and enable instructors to determine whether and how the environment actually affects student performance.Recommendations -Further work on refining the process applied to the data is recommended. The log data should be time-sliced and processed to determine whether and how the student's level and type of activity changes over time. More powerful machine learning classification techniques shouldalso be tested to determine whether it can improve the classification accuracy.Research Implications -These types of visualizations and predictive models could be used to monitor the student or class which requires immediate and specific pedagogical adjustments.

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Section: Introductionmentioning

confidence: 99%

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Raga

Raga²

2017

IJCSR

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“…learning style (active) used in a web usage mining procedure and consequently, pre-processing of the web log file becomes imperative. To this end, the design of a data e-learning web-house as a supporting structure for future personalized elearning systems has been proposed [64]. Hang jinghua [65] propose a Semantic Web Based Personalized Learning Service for programming courses in e-learning.…”

Section: Related Work (Adaptive E-learning Models)mentioning

confidence: 99%

Personalized Students’ Profile Based On Ontology and Rule-based Reasoning

Nafea¹,

Μαγλαράς²,

Siewe³

et al. 2016

EAI Endorsed Transactions on e-Learning

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Nowadays, most of the existing e-learning architecture provides the same content to all learners due to "one size fits for all" concept. E-learning refers to the utilization of electronic innovations to convey and encourage training anytime and anywhere. There is a need to create a personalized environment that involves collecting a range of information about each learner. Questionnaires are one way of gathering information on learning style, but there are some problems with their usage, such as reluctance to answer questions as well as guesses the answer being time consuming. Ontology-based semantic retrieval is a hotspot of current research, because ontologies play a paramount part in the development of knowledge. In this paper, a novel way to build an adaptive ontological student profile by analysis of learning patterns through a learning management system, according to the Felder-Silverman learning style model (FSLSM) and Myers-Briggs Type Indicator (MBTI) theory is proposed

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“…Now a day various electronic resources such as e-commerce, e-governance, digital libraries, social network sites leads to the information overload problem. Recommendation Systems for a specific domain are the trend choice to reduce this problem to its maximum extent by suggesting probably relative pages to the user [11,12]. One of the fields that got affected by the information overload problem is education or e-learning domain.…”

Section: Related Workmentioning

confidence: 99%

Personalized Context Aware Assignment Recommendations in E-Learning System

Venu¹,

Sammulal²

2016

IJCA

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E-Learning is the technology that targets the community seeks learning through various means such as material, information, courses to facilitate the ease of access. Online delivery of educational instruction provides the opportunity to bring colleges and universities new energy, students, and revenues. However e-learning system has rapid development in making activities of learning online and providing a vast set of resources for the material and online assignments to complete. Although personalized e-learning systems developed and provides services they limited to focus their recommendations of the material is only about the student"s level interest of surfing on the learning material but never considered the level of understanding of the learning material. This system developed a model that aim to recommend the assignments and material of the course to the student based on the level of understanding by analyzing the performance of the student in past. Experimental results on the proposed recommender system exhibited significant results than the traditional elearning system. This shown the impact of personalized assignment recommendations in improving the student"s interest towards the course.

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Data Web house to support Web intelligence in e-learning environments

Cited by 8 publications

References 19 publications

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Personalized Students’ Profile Based On Ontology and Rule-based Reasoning

Personalized Context Aware Assignment Recommendations in E-Learning System

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