DSpace Collection: 4. LN: Software and Documentation

Social Network Analysis Tool for Navigation Support of Learners in Learning Networks

Mon, 15 Sep 2008 13:33:27 GMT

Title: Social Network Analysis Tool for Navigation Support of Learners in Learning Networks

Authors: Drachsler, Hendrik; Kone, Malik

Abstract: An analysis tool created in Netlogo to analyse emergent effects of recommender systems in Learning Network experiments. It is mainly used to observe and compare the “variety” and the “length” of learning paths in a Learning Networks. In order to run the analysis tool you have to create a control group (cc-data.txt) and experimental groups file (ec-data.txt). Please keep the semantic of the present files like: User ID "Username" Course ID 80 "Username" 23 80 "Username" 26 87 "Username" 23 The data is ordered according to the completion of the courses. Thus, the learner that first finished a course stands on top of the list. All other completed courses are listed according to the time stamp they were successfully completed. For further information please have a look at: - http://dspace.ou.nl/handle/1820/938 - http://dspace.ou.nl/handle/1820/1186

Description: This is free software, you may use it and distribute it under the following conditions: Copyright (c) 2008, TENCompetence Foundation Board All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the TENCompetence Foundation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE

data sets Simulations in articulating light-weight PRS

Sat, 13 Sep 2008 11:02:09 GMT

Title: data sets Simulations in articulating light-weight PRS

Authors: Van den Berg, Bert

Abstract: Data sets used for step 1, 2 and 3 in order to research effectiveness and effiency of different Recommondation strategies used in a simulation of a Pesonalised Recommender System

Description: The data sets are output of 3 different steps in the development a simulations of a PRS as described in chapter 3.3: Simulations in articulating light-weight PRS A case for Pedagogy-oriented and Rating-based Hybrid Recommendation Strategies Rob Nadolski, Bert van den Berg, Adriana Berlanga, Hans Hummel, Hendrik Drachsler, Rob Koper & Peter Sloep Open University of the Netherlands, OTEC, Centre for Technology Enhanced Learning As the study consists of three incremental steps - each using a conceptual simula-tion model and a Netlogo (Wilensky, 1999) implementation - three separate method and results sections will be distinguished. A method section describes hy-potheses for the simulation study and its setup of each step. Netlogo is used to perform the simulation runs. The outcomes of such runs validate the version of the conceptual simulation model in that step, and guide further articulation of the conceptual simulation model. Our study started with a focussed expert discussion in which variables, their relationships, and associated literature with existing re-search were identified as input for all steps. In the first step, focus is given towards simplicity. The second step focuses on more reality, whereas the third step focuses on practical implementation issues. Each model will be elaborated in three parts: model variables, measurement variables, and recommendation strategies, followed by the setup of the simulation. Finally, conditions and treatments that are included in the simulation runs of that step will be described. Each conceptual simulation model is largely in line with the model described by Koper (2005). The main goal of the final version of the conceptual simulation model is that it represents the minimized set of LA- and learner characteristics that can support the included RS. Every condition in the simulation was replicated 12 times (i.e., N = 12 runs) to justify the use of classical statistic techniques on resulting data (Law and Kelton, 2000: p. 496), analyzed with SPSS version 15. Each condition included three treatment groups (O, P, OP) and one control group (C). Six conditions were included: goal (2) x sub domain (3). A low-end goal and a high-end goal were taken (see Tables 3.3.3 and 3.3.4). Either, 2, 3 or 4 sub do-mains were taken as they were all regarded to be representative for studies in higher distance education. In all six conditions, each group included 250 learners that could choose from 400 LAs. For all runs, only ‘graduates’ or ‘dropouts’ were allowed after run length. In other words, no participants were ‘still studying’ after run length. To check the robustness of the conceptual simulation model other conditions have been simulated as well.

Personal Recommender Simulation

Sat, 22 Mar 2008 16:34:32 GMT

Title: Personal Recommender Simulation

Authors: Van den Berg, Bert; Nadolski, Rob; Drachsler, Hendrik; Berlanga, Adriana; Hummel, Hans; Koper, Rob; Sloep, Peter

Abstract: The main goal of our project is to research and develop technologies that support users in Learning Networks. One of the questions in this research is how to tackle the problem of the difficulty for learners to navigate through the network: what is the most effective way to reach the learning target or which course can be studied best after finishing a particular one? The aim of this program is to simulate recommendations (strategies) of learning actions (LA's) for learners in a learning network with different (sub) domains, targets, preferences and competences. It is used to explore and to research the effectiveness of different recommendation strategies in various settings.

Description: The simulation and its background is described in detail in: Simulating [in the search] for lightweight Personalised Recommendation Systems in Learning Networks: A case for Pedagogy-Oriented Rating-based Hybrid Recommendation Strategies Rob J. Nadolski, Bert van den Berg, Adriana J. Berlanga, Hendrik Drachsler, Hans G.K. Hummel, Rob Koper and Peter B. Sloep. Open University of the Netherlands.

Personal Recommender System to advice Learning Tasks in Moodle

Mon, 16 Apr 2007 15:59:10 GMT

Title: Personal Recommender System to advice Learning Tasks in Moodle

Authors: Drachsler, Hendrik; Van den Berg, Bert

Abstract: A Personal Recommender System (PRS) has been created for Moodle to be able to recommend learning tasks to learners. The PRS was evaluated in an experiment of the internal ISIS project.

Description: License text: The tracking module is created as a Moodle module. Copyright for Moodle lies with Martin Dougiamas. Moodle is a free software, available under the GNU General Public License. Therefore, the scorecard module is also available under the GNU General Public License. Together with this notice, you should have received the COPYRIGHT notice belonging to the Moodle software. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA