Group-awareness for Mobile Cooperative Learning Roc Messeguer, Leandro Navarro, Angelica Reyes Department of Computer Architecture Technical University of Catalonia Good morning. I’m Roc Messeguer from Technical University of Catalonia. Excuse me for my poor English. I’m going to talk about “Group-awareness for Mobile Cooperative Learning”

INTRODUCTION Cooperative learning is an instruction method based on students working together in small groups to accomplish shared learning goals. The location of every significant element in the workplace is a rich source of information to understand the structure and performance of the collaborative activities. The problem of automating group awareness information into CSCL applications supporting cooperative learning activities, without passing this burden to group participants or overloading the instructor. First, it’s the introduction. The environment of this work is Collaborative learning, with a special interest in students groups. The tool, that we used to understand this Collaborative learning activities, is the location of every important element in the workspace or classroom. The problem that we affront is to automate group awareness information into Computer-Supported Collaborative Learning (CSCL) applications, without introducing additional burden to human participants.

OUR APPROACH / CONTRIBUTION We intend to use location information to automatically infer contextual information that facilitates CSCL support. Exploring ways to support activities requiring work in groups This information can be applied during the activity to inform the instructor about the current organization of the classroom to support students whenever they are in a given group to collect log information for further evaluation. Now, I’m going to talk about our approach to this problem. We intend to use location information to automatically infer contextual information that facilitates CSCL support. The main contribution of this work is in exploring ways to support in collaborative learning activities, and in general in activities requiring work in groups, in detecting the formation, composition and reorganization of groups of students automatically. This information can be applied during the activity to inform the instructor about the current organization of the classroom to support students whenever they are in a given group to collect log information for further evaluation.

GROUP AWARENESS MIDDLEWARE Information sources The middleware picks up information from the physical level that provides location information The middleware should consider: previously defined context information (e.g. the characteristics of the classroom: number and location of tables, whiteboards, etc.) previously defined rules (e.g. common rules to assign group membership based on proximity to a certain artifact). We designed a middleware to support this approach. The more important parts of this middleware are the information sources that we need to manage groups in CSCL. The middleware picks up information from the physical level using different location technologies that provides location information (e.g. relative location, physical proximity to things such as tables, etc) Besides this information, the middleware should also consider previously defined context information (e.g. the characteristics of the classroom: number and location of tables, whiteboards, etc.) and also previously defined rules (e.g. common rules to assign group membership based on proximity to a thing).

GROUP AWARENESS MIDDLEWARE Information sources The middleware picks up information from the physical level that provides location information The middleware should consider: previously defined context information (e.g. the characteristics of the classroom: number and location of tables, whiteboards, etc.) previously defined rules (e.g. common rules to assign group membership based on proximity to a certain artifact). For example: this is a classroom. The blue squares represent tables, and green balls represent people. We defined context information such as the number and location of tables. We defined rules such as students near each table are assigned to this group. And finally, a location technology tells to middleware, in real time, the location of each student.

GROUP AWARENESS MIDDLEWARE Rules The pattern of a previously defined set of rules: If (condition == TRUE) then {specific action} has to be applied. Types of rules: Rules to create groups Rules to destroy groups Rules to belong to a specific group Rules to assign a specific role to persons belonging to a group Besides positioning information, detecting groups and assigning roles are processes that require a previously defined set of rules the pattern: “If there is a condition that being evaluated is true then a specific action has to be applied.” These rules are a key part of the management module of the middleware, which is responsible for processing the location information and sending the result to the upper application level. Types of rules: Rules to create groups Rules to destroy groups Rules to belong to a specific group Rules to assign a specific role to people belonging to a group

GROUP AWARENESS MIDDLEWARE Rules The pattern of a previously defined set of rules: If (condition == TRUE) then {specific action} has to be applied. Types of rules: Rules to create groups Rules to destroy groups Rules to belong to a specific group Rules to assign a specific role to persons belonging to a group For example: This is the same classroom than before. We defined a rule. We defined / created groups in each work table. Every student near a table is assigned to this group.

GROUP AWARENESS MIDDLEWARE This is a chart of our middleware. Our previously defined information: context and rules The location information source It sends the result information, groups and members information, to the application level.

EVALUATION To evaluate how effective and useful is group awareness using location information Implemented a simplified version of the middleware Developed a instant group collaboration application Experiment with real users in collaborative learning activities Our intention in this evaluation was to assess how CSCL applications with automatic group awareness that assist group interactions result in any measurable improvement in the learning process. To evaluate how effective and useful is group awareness using location information We implemented a simplified version of the middleware We developed a instant group collaboration application And finally we made some experiments with real users in collaborative learning activities Our intention in this evaluation was to assess how CSCL applications with automatic group awareness that assist group interactions result in any measurable improvement in the learning process.

EXPERIMENTS The collaborative learning methodology followed in the experiments performed was a jigsaw. The stages of the jigsaw activity we followed are: Introduction of the topic (whole class) The teams go over the problem in question and assign a section to each member (by group) Individual work of each section of the problem (by student) Expert groups work to master the concepts of their section (by group) Home groups work to connects the various section to answer the problem in question (by group) Evaluation (by student and/or by group) The collaborative learning methodology followed in the experiments performed was a jigsaw. The stages of the jigsaw activity we followed are: First, introduction of the topic to whole class Then, the teams go over the problem in question and they self assign a section of this problem to each member (activity by group) Individual work of each section of the problem (activity by student) Students that worked the same part of problem from a group, the expert groups. They work to master the concepts of their section (activity by group) All students come to initial groups and work to connects the various section to answer the problem in question (activity by group) And finally, the activity evaluation (a quiz by student and a final report by group)

EVALUATION OF LOCATION TECHNOLOGY Initial tests with RFID tags (bad results) Triangulation based on WiFi (good results) Placelab Software The system can detect which students are on each group based on the location of each unique MAC address of the laptops they are carrying. Location of students in the classroom (cm), home group state, (27 students, 30 minutes, 2 samples/min). Now, I’m going to talk about location technologies. First, we started to work with RFID. But we found lots of technical problems, for example, an important number of errors during reading RFID tags. Finally, we used a kind of triangulation based on WiFi access points. We modified Placelab software to integrate into our middleware. For example, this chart shows the location of 27 students working for 30 minutes with 2 samples per minute. The system can detect which students are on each group based on the location of their own WiFi MAC address of the laptops they are carrying.

INSTANT GROUP COLLABORATION APPLICATION The goal of this application is to offer to each student a shared interaction space, permissions to access documents and disseminate events produced by other people in the same physical group. The application offers a shared virtual folder for the group where any member can find all the documents that everyone in the group has made public. The applications running in each laptop interact with other application instances in a peer-to-peer way based on Pastry, a distributed hash table routing. After this slide about location technologies, I’m going to talk about The Instant Group Collaboration Application. The goal of this application is to offer to each student a shared interaction space, permissions to access documents and disseminate events produced by other people in the same physical group. The application offers a shared virtual folder for the group where any member can find all the documents that everyone in the group has made public. The applications running in each laptop interact with other application instances in a peer-to-peer way based on Pastry, a distributed hash table routing.

EXPERIMENTAL DATA AND EVALUATION Opinions of students obtained from a “quick” questionnaire (what is good and what could be better) Comments from the instructors Individual quiz (measures quality of knowledge) Group document (measures quality of outcome of the activity) Table 2. Individual qualifications out of 10 (scores from a quiz on the topics covered in the class) Location Group (IGC) Redianet group (laptops) Laptop group (no app) Control group (desktops) Average 8.2 7.9 7.4 8 Std Deviation 2.5 2.6 2.3 The experimental data to assess the improvement in the learning process are:. Opinions of students obtained from a “quick” questionnaire (In a small piece of paper, they write what was good and what could be better about the activity) Comments from the instructors Individual quiz about concepts, that measures quality of knowledge And last one, a final group report that measures quality of outcome of the activity. In this table we have the average qualification of quiz. In the last, right, column, students did the activity with Computer desktop, here (next one) with laptops, next with laptops + a group awareness software called Redianet, and in the first, left column with laptops + our Instant Group Collaboration with location. As conclusions from the analysis of individual: The average individually qualification doesn’t depend, or at least not clearly, from the scenario where the activity was performed (see table 2). And in table 3, we have the average qualification of final group report. We have the same scenarios than above. As conclusions from the analysis of group scores we can infer: The qualifications obtained by the group, do have a clear dependency with the scenario where the activity was performed (see table 3). The best qualification comes from using location group awareness. That shows how helpful are the WiFi location technologies for quality of the outcome from students groups. Table 3. Group qualifications out of 10 (scores from evaluating the activity group report) Location Group (IGC) Redianet group (laptops) Laptop group (no app) Control group (desktops) Average 9.1 8.4 7.0 6.25 Std Deviation 1.9 2.6 2.3 1.2

CONCLUSIONS Experimental validation and comparison among different settings has shown that location technologies can be effectively used as a basis for a middleware for the dynamic management of context information in terms of groups. Deriving group membership information from location information based on WiFi networks is technically viable and can be incorporated in CSCL applications. The use is beneficial for group participants for CSCL applications. The effect can be perceived in terms of user satisfaction and improvement on the learning outcomes and thus in student’s qualifications. And last slide, conclusions. Experimental validation and comparison among different settings has shown that location technologies can be effectively used as a basis for a middleware for the dynamic management of context information in terms of groups. Deriving group membership information from location information based on WiFi networks is technically viable and can be incorporated in CSCL applications. The use is beneficial for group participants for CSCL applications. The effect can be perceived in terms of user satisfaction and improvement on the learning outcomes and thus in student’s qualifications.

Group-awareness for Mobile Cooperative Learning Roc Messeguer, Leandro Navarro, Angelica Reyes Department of Computer Architecture Technical University of Catalonia Thanks for your attention. And I hope you have enjoy it.