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multiple reports, analytics, and notifications that keep learners focused, and provide data that can improve learning methodologies. By providing the learner's status summary such as success and failure rates, assignment due status etc., the IntelliBoard.net can help institutions in identifying at-risk students.

Figure 9 multiple reports, analytics, and notifications that keep learners focused, and provide data that can improve learning methodologies. By providing the learner's status summary such as success and failure rates, assignment due status etc., the IntelliBoard.net can help institutions in identifying at-risk students.

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Education Prediction Rules (EPRules): EPRules [63] is a java-based graphical tool used to solve the prediction rule discovery in adaptive systems in a web-based learning environment. This tool can be used even by course developers or teachers who are not expert in data mining. The Data input component of this tool allows to open an existing database or create a new one using course usage file. The Data view component helps in visualizing the students’ course usage data. The Prediction rule discovery component ( shown in figure 1) allows selecting one of the several rule discovery algorithms, to choose the specific execution parameters for the chosen algorithm, to select the subjective restriction ( such as the number of chapters or number of students), and to choose the objective evaluation function. The last component Knowledge view displays the discovered rules, conditions of the rules and evaluation parameters. Graphical Interactive Student Monitoring (GISMO): GISMO? is a graphical interactive monitoring tool that provides useful visualizations of students! activities in online courses to instructors. With GISMO instructors can examine various aspects of distance students, such as attendance to courses, reading of materials, and submission
Education Prediction Rules (EPRules): EPRules [63] is a java-based graphical tool used to solve the prediction rule discovery in adaptive systems in a web-based learning environment. This tool can be used even by course developers or teachers who are not expert in data mining. The Data input component of this tool allows to open an existing database or create a new one using course usage file. The Data view component helps in visualizing the students’ course usage data. The Prediction rule discovery component ( shown in figure 1) allows selecting one of the several rule discovery algorithms, to choose the specific execution parameters for the chosen algorithm, to select the subjective restriction ( such as the number of chapters or number of students), and to choose the objective evaluation function. The last component Knowledge view displays the discovered rules, conditions of the rules and evaluation parameters. Graphical Interactive Student Monitoring (GISMO): GISMO? is a graphical interactive monitoring tool that provides useful visualizations of students! activities in online courses to instructors. With GISMO instructors can examine various aspects of distance students, such as attendance to courses, reading of materials, and submission
Fig. 2. A graph by GISMO reporting the student's accesses to the course. of assignments. GISMO, in tandem with Learning Management System Moodle, can provide comprehensive visualizations that give an overview of the whole class, not only a specific student or a particular resource [27].
Fig. 2. A graph by GISMO reporting the student's accesses to the course. of assignments. GISMO, in tandem with Learning Management System Moodle, can provide comprehensive visualizations that give an overview of the whole class, not only a specific student or a particular resource [27].
Fig. 3. A General Overview of TADA-Ed[ 66 ]
Fig. 3. A General Overview of TADA-Ed[ 66 ]
DataShop: DataShop® is a free data repository and web application for learning science researchers [71][72]. It can store many types of data from interactive learning environments such as intelligent tutoring systems, virtual labs, simulations, and games. Datashop provides only exploratory statistical analysis of learning data. However, it allows users to export the data in the formats suitable to other statistical analysis tools. The amount of data in DataShop is constantly growing. Researchers have utilized DataShop to explore learning issues in a variety of educational domains. These include, but are not limited to, collaborative problem-solving in Algebra [73]. self-explanation in Physics [74], the effectiveness of worked examples in a Stoichiometry tutor [75] and the optimization of knowledge component learning in Chinese [76].
DataShop: DataShop® is a free data repository and web application for learning science researchers [71][72]. It can store many types of data from interactive learning environments such as intelligent tutoring systems, virtual labs, simulations, and games. Datashop provides only exploratory statistical analysis of learning data. However, it allows users to export the data in the formats suitable to other statistical analysis tools. The amount of data in DataShop is constantly growing. Researchers have utilized DataShop to explore learning issues in a variety of educational domains. These include, but are not limited to, collaborative problem-solving in Algebra [73]. self-explanation in Physics [74], the effectiveness of worked examples in a Stoichiometry tutor [75] and the optimization of knowledge component learning in Chinese [76].
Student Activity Monitoring using Overview Spreadsheets (SAMOS): SAMOS is an information system that facilitates the automatic generation of weekly monitoring reports derived from data contained in online collaborative learning environments [78]. It allows instructors to view classification of students according to the activity level which can be helpful in identifying at-risk students. It uses Excel’s numerical, graphical and programming capabilities to generate weekly reports from the student activities stored on the server of the learning management system and sends them to the instructors through email.
Student Activity Monitoring using Overview Spreadsheets (SAMOS): SAMOS is an information system that facilitates the automatic generation of weekly monitoring reports derived from data contained in online collaborative learning environments [78]. It allows instructors to view classification of students according to the activity level which can be helpful in identifying at-risk students. It uses Excel’s numerical, graphical and programming capabilities to generate weekly reports from the student activities stored on the server of the learning management system and sends them to the instructors through email.
Meerkat-ED: Meerkat-ED is a tailored version of Meerkat social network analysis tool® allowing instructors to evaluate student activities in asynchronous discussion forums of online courses. Meerkat-ED analyzes the structure of these interactions using social network analysis techniques including community mining. It prepares and visualizes overall snapshots of participants in the discussion forums, their interactions, and the leader/peripheral students in these discussions. Moreover, it analyzes the contents of the exchanged messages in this discussions by building an information network of terms and using community mining techniques to identify the topics discussed. Meerkat-ED creates a hierarchical summarization of these discussed topics in the forums, which gives the instructor a quick view of what is under discussion in these forums. It further illustrates how much each student has participated in these topics, by showing their centrality in the discussions on that topic, the number of posts, replies, and the portion of terms used by that student in the discussions [24]. Fig. 6. A snapshot of Meerkat-ED toolbox [80]
Meerkat-ED: Meerkat-ED is a tailored version of Meerkat social network analysis tool® allowing instructors to evaluate student activities in asynchronous discussion forums of online courses. Meerkat-ED analyzes the structure of these interactions using social network analysis techniques including community mining. It prepares and visualizes overall snapshots of participants in the discussion forums, their interactions, and the leader/peripheral students in these discussions. Moreover, it analyzes the contents of the exchanged messages in this discussions by building an information network of terms and using community mining techniques to identify the topics discussed. Meerkat-ED creates a hierarchical summarization of these discussed topics in the forums, which gives the instructor a quick view of what is under discussion in these forums. It further illustrates how much each student has participated in these topics, by showing their centrality in the discussions on that topic, the number of posts, replies, and the portion of terms used by that student in the discussions [24]. Fig. 6. A snapshot of Meerkat-ED toolbox [80]
Fig. 7. The main view of KBDeX [82 ]
Fig. 7. The main view of KBDeX [82 ]
objective is to empower teachers to manage the evolution of the students in an online course. It is an Open source and multi-platform learning analytics dashboard plug-in. It allows teachers to see a global view of the performance of the students, check the evolution of any course, and control and check an alarm system to send messages to the students. Similarly, it enables students viewing their performances, see the evolution of the course and receive or send alert messages. Fig. 40. SmartKlass.
objective is to empower teachers to manage the evolution of the students in an online course. It is an Open source and multi-platform learning analytics dashboard plug-in. It allows teachers to see a global view of the performance of the students, check the evolution of any course, and control and check an alarm system to send messages to the students. Similarly, it enables students viewing their performances, see the evolution of the course and receive or send alert messages. Fig. 40. SmartKlass.
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