An Authoring Tool to Support the Design and Use of Theory-Based Collaborative Learning Activities

Isotani, Seiji; Mizoguchi, Riichiro; Isotani, Sadao; Capeli, Olimpio M.; Isotani, Naoko; Albuquerque, A.R.P.L. de

doi:10.1007/978-3-642-13437-1_10

“…They can be divided into pattern based or graphical based. In the context of pattern based, COLLAGE 28 is a collaborative learning design authoring tool based on patterns called Collaborative Learning Flow Patterns (CLFPs) 29 that include a set of best practices, while CHOCOLATO 30 is an authoring tool guided by different pedagogical models that uses an ontology. Some graphical representation tools are MOT 31, LAMS 32, 33, ASK‐LDT 34, eLiveSuite 35, or UML based frameworks like the described in Ref.…”

Section: Related Workmentioning

confidence: 99%

CourseEditor: A course planning tool compatible with IMS‐LD

Cuevas

¹

,

Muñoz‐Merino

²

,

Fernandez‐Panadero

³

et al. 2010

Comp Applic In Engineering

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For the successful adoption of Computer Based Learning (CBL), it is necessary to provide teachers with user-friendly authoring tools that guide them in the process of planning a course. It is important that the documents generated by these tools are compliant with CBL standards to ensure that the instructional designs made by the teachers remain valid regardless of the Learning Management System (LMS) used to deliver the course. IMS-Learning Design (IMS-LD) is one of the most accepted specifications by the educational community for modelling of learning processes. There are some authoring-tools that allow export learning designs in IMS-LD but they are not course-planning oriented. In addition, a big challenge is how to improve the user interfaces of these tools in order to be easier to use for regular teachers. This paper presents CourseEditor, a course planning authoring tool that allows a teacher to describe the complete planning of a course (objectives, contents, methodology and evaluation) and export the results in an IMS-LD compatible format. The paper provides a novel modelling of course planning that takes into account ideas from different instructional theories. CourseEditor combines power and flexibility, with the simplicity of an interface that can be used by teachers with no technical background. The paper illustrates the use of the tool creating a course on Telematic Services in the context of a Telecommunications Engineering degree. In addition, the relationships of course planning and IMS-LD are presented, showing which information of course planning can be represented in IMS-LD and which not.

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“…Study Participants [50] and [89] 50 tutoring sessions [41] 39 participants [45] 25 students [25] 6 course authors [36] 4 classrooms [24] 2 classrooms, 58 students [56] 18 learners [2] 106 students [47] 105 students In order to reflect on the value of our research, we begin with a comparison to related work conducted in the area of e-learning and intelligent tutoring systems.…”

Section: In Contrast To Studies With Human Studentsmentioning

confidence: 99%

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Champaign

¹

2010

Advances in Artificial Intelligence

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In this thesis, we present an artificial intelligence approach for tutoring students in environments where there is a large repository of possible learning objects (e.g. texts, videos). In particular, we advocate that students learn on the basis of past experiences of peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring, where a learning object's history-of-use is retained and leveraged to instruct future students. We offer three distinct models that serve to deliver the required intelligent tutoring: (i) a curriculum sequencing algorithm selecting which learning objects to present to students based on benefits to knowledge obtained by similar peers (ii) a framework for peers to provide commentary on the learning objects they've experienced (annotations) together with an algorithm for reasoning about which annotations to present to students that incorporates modeling trust in annotators (i.e. their reputation) and ratings provided by students (votes for and against) for the annotations they have been shown (iii) an opportunity for peers to guide the growth of the corpus by proposing divisions of current objects, together with an algorithm for reasoning about which of these new objects should be offered to students in order to enhance their learning. All three components are validated as beneficial in improving the learning of students. This is first of all achieved through a novel approach of simulated student learning, designed to enable the tracking of the experiences of a very large number of peers with an extensive repository of objects, through the effective modeling of knowledge gains. This is also coupled with a preliminary study with human participants that confirms the value of our framework. In all, we offer a rich and varied role for peers in guiding the learning of students in intelligent tutoring environments, made possible by careful modeling of the students who are being taught and of the potential benefits to learning that would be derived with the selection of appropriate tutorial content.iii Acknowledgements First and foremost I would like to thank my supervisor, Professor Robin Cohen, for guiding me through the completion of this dissertation and my PhD studies. Robin overwhelmed me with the amount of time and effort she put in to helping me complete my doctoral work. My committee members, Professor Edward Lank and Professor Peter van Beek, have provided detailed and helpful feedback throughout the process, which I greatly appreciate. Thanks as well to Professor Gord McCalla for his helpful advice. My appreciation goes out to the anonymous reviewers who considered earlier versions of this work and provided feedback during conference and journal submissions over the last 4 years.I would like to acknowledge Ahmed Ataullah, Simina Brânzei, John Doucette and Lachlan Dufton for being wonderful officemates: always willing to talk through issues I was working on, go for coffee and share a laugh. I greatly enjoyed working in the AI Group at the University of ...

show abstract

“…Participants [50] and [89] 50 tutoring sessions [41] 39 participants [45] 25 students [25] 6 course authors [36] 4 classrooms [24] 2 classrooms, 58 students [56] 18 learners [2] 106 students [47] 105 students In order to reflect on the value of our research, we begin with a comparison to related work conducted in the area of e-learning and intelligent tutoring systems.…”

Section: Studymentioning

confidence: 99%

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Champaign

¹

,

Cohen

²

2010

Intelligent Tutoring Systems

1

0

View full text Add to dashboard Cite

In this thesis, we present an artificial intelligence approach for tutoring students in environments where there is a large repository of possible learning objects (e.g. texts, videos). In particular, we advocate that students learn on the basis of past experiences of peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring, where a learning object's history-of-use is retained and leveraged to instruct future students. We offer three distinct models that serve to deliver the required intelligent tutoring: (i) a curriculum sequencing algorithm selecting which learning objects to present to students based on benefits to knowledge obtained by similar peers (ii) a framework for peers to provide commentary on the learning objects they've experienced (annotations) together with an algorithm for reasoning about which annotations to present to students that incorporates modeling trust in annotators (i.e. their reputation) and ratings provided by students (votes for and against) for the annotations they have been shown (iii) an opportunity for peers to guide the growth of the corpus by proposing divisions of current objects, together with an algorithm for reasoning about which of these new objects should be offered to students in order to enhance their learning. All three components are validated as beneficial in improving the learning of students. This is first of all achieved through a novel approach of simulated student learning, designed to enable the tracking of the experiences of a very large number of peers with an extensive repository of objects, through the effective modeling of knowledge gains. This is also coupled with a preliminary study with human participants that confirms the value of our framework. In all, we offer a rich and varied role for peers in guiding the learning of students in intelligent tutoring environments, made possible by careful modeling of the students who are being taught and of the potential benefits to learning that would be derived with the selection of appropriate tutorial content.iii Acknowledgements First and foremost I would like to thank my supervisor, Professor Robin Cohen, for guiding me through the completion of this dissertation and my PhD studies. Robin overwhelmed me with the amount of time and effort she put in to helping me complete my doctoral work. My committee members, Professor Edward Lank and Professor Peter van Beek, have provided detailed and helpful feedback throughout the process, which I greatly appreciate. Thanks as well to Professor Gord McCalla for his helpful advice. My appreciation goes out to the anonymous reviewers who considered earlier versions of this work and provided feedback during conference and journal submissions over the last 4 years.I would like to acknowledge Ahmed Ataullah, Simina Brânzei, John Doucette and Lachlan Dufton for being wonderful officemates: always willing to talk through issues I was working on, go for coffee and share a laugh. I greatly enjoyed working in the AI Group at the University of ...

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An Authoring Tool to Support the Design and Use of Theory-Based Collaborative Learning Activities

Cited by 10 publications

References 5 publications

CourseEditor: A course planning tool compatible with IMS‐LD

CourseEditor: A course planning tool compatible with IMS‐LD

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

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