MATEP: Monitoring and Analysis Tool for E-Learning Platforms

Zorrilla, Marta E.; Álvarez, Elena

doi:10.1109/icalt.2008.33

Cited by 23 publications

(18 citation statements)

References 3 publications

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“…Sophisticated systems might even recommend learning activities, predict a student's success or give advice. Examples of indicators are: Number of content pages viewed per student [58], Number of threads started per student [41,45,55], Number of messages read on forum per student [11,41,45,58], Number of messages read by the user in relation to number of messages available [12], Content currently read by one or more students [12], Relation between keywords and students [47], Frequency a student used each keyword [47], Student risk group/status [3], Advice to uncommunicative advanced students to help others [37].…”

Section: Perspective Categoriesmentioning

confidence: 99%

“…number of incorrect answers per question in a quiz [1] X X Number of unique users per resource (weekly, daily, hourly) [55] X X Resources frequently used together (forum, mail, etc.) [27] X X X Top5 pages/resources [58] X X into receptive and active activities: Receptive activities are accessing or reading resources, listening to audio, or watching lecture videos. Active participation is based on contributions on the forums, discussion participation, editing wiki pages, uploading assignments, as well as sharing resources.…”

Section: Perspective Categoriesmentioning

confidence: 99%

“…For instance, the indicators could measure how active a student is. Activity is derived from the student's generated data, and it can be divided [42,45,55] X X Number of assignments submitted per student [41,54] X X X Relation between keywords and students [47] X X Student risk group/status [3] X X X X Social network derived from email exchange data per student [51] X X Keyword analysis derived from email exchange data per student [51] X X Resources watched (read) in a session per student (via webcam) [27] X X X Students' gender [26,58] X X X Students' academic level [58] X X X Students' tags of learning resources [51] X X X Work amount (message dimension per user) per course phase [49] X X Number of participants per group [25,41] X X Number of files per group [41] X X Advice to groups concerning uncommunicative behavior [37] X X Global accesses to the course [45] X X Resources that have NOT been accessed (weekly, daily, hourly) [55] X X Learning paths analysis [51,58] X X SNA: actor's degree centrality per course phase [49] X X X Students' comprehension of topics (based on annotations) [1] X Avg. overall quiz score [1] X X Clusters of students who made a (specific) mistake [46,50] X X Advice to the teacher concerning excellent and weak students [37] X X Avg.…”

Section: Perspective Categoriesmentioning

confidence: 99%

“…We included the following LA projects in our study on indicators: LOCO-Analyst [1,4], TADA-Ed [46], Data Model to Ease Analysis and Mining [38], Student Inspector [50], MATEP [56][57][58], CourseVis [43,45], GISMO [44], Course Signals [3], Check My Activity [25], Moodog [54,55], TrAVis [41,42], Moodle Mining Tool [48], EDM Vis [34], AAT [29], Teacher ADVisor [37], E-learning Web Miner [26], ARGUNAUT [30], Biometricsbased Student Attendance Module [27], CAMera and ZeitgeistDashboard [51,52], Student Activity Meter [28], Discussion Interaction Analysis System (DIAS) [8][9][10][11], CoSyLMSAnalytics [49], Network Visualization Resource and SNAPP [5,6,17,18], i-Bee [47], iHelp [12], and Participation Tool [32]. We limited our analysis on available indicators to these projects because they have been presented in literature as state-of-the-art LA-tools, which can already be used by their intended target users.…”

Section: Learning Analytics Toolsmentioning

confidence: 99%

See 3 more Smart Citations

Supporting action research with learning analytics

Dyckhoff

Lukarov

Muslim

et al. 2013

Proceedings of the Third International Conference on Learning Analytics and Knowledge

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Learning analytics tools should be useful, i.e., they should be usable and provide the functionality for reaching the goals attributed to learning analytics. This paper seeks to unite learning analytics and action research. Based on this, we investigate how the multitude of questions that arise during technology-enhanced teaching and learning systematically can be mapped to sets of indicators. We examine, which questions are not yet supported and propose concepts of indicators that have a high potential of positively influencing teachers' didactical considerations. Our investigation shows that many questions of teachers cannot be answered with currently available research tools. Furthermore, few learning analytics studies report about measuring impact. We describe which effects learning analytics should have on teaching and discuss how this could be evaluated.

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Section: Perspective Categoriesmentioning

confidence: 99%

Section: Perspective Categoriesmentioning

confidence: 99%

Section: Perspective Categoriesmentioning

confidence: 99%

Section: Learning Analytics Toolsmentioning

confidence: 99%

See 2 more Smart Citations

Supporting action research with learning analytics

Dyckhoff

Lukarov

Muslim

et al. 2013

Proceedings of the Third International Conference on Learning Analytics and Knowledge

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“…Mostow et al (2005) describe a tool which uses log files in order to represent the instructor-student interaction in hierarchical structure. MATEP (Zorrilla & Álvarez, 2008) is another tool acting at two levels. Firstly, it provides a mixture of data from different sources suitably processed and integrated.…”

Section: Literature Reviewmentioning

confidence: 99%

E-Learning Platform Usage Analysis

Valsamidis¹,

Kontogiannis²,

Kazanidis³

et al. 2011

IJELL

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E-learning is technology-based learning, such as computer-based learning, web-based learning, virtual classroom, and digital collaboration. The usage of web applications can be measured with the use of indexes and metrics. However, in e-Learning platforms there are no appropriate indexes and metrics that would facilitate their qualitative and quantitative measurement. The purpose of this paper is to describe the use of data mining techniques, such as clustering, classification, and association, in order to analyze the log file of an eLearning platform and deduce useful conclusions. Two metrics for course usage measurement and one algorithm for course classification are used. A case study based on a previous approach was applied to e-Learning data from a Greek University. The results confirmed the validity of the approach and showed a strong relationship between the course usage and the corresponding students' grades in the exams.From a pedagogical point of view this method contributes to improvements in course content and course usability and the adaptation of courses in accordance with student capabilities. Improvement in course quality gives students the opportunity of asynchronous study of courses with actualized and optimal educational material and, therefore, higher performance in exams.It should be mentioned that even though the scope of the method is on e-Learning platforms and educational content, it can be easily adopted to other web applications such as e-government, ecommerce, e-banking, blogs, etc.

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