Categorization of learning design courses in virtual environments
A categorization of learning design for courses in virtual environmentsVirginia Rodés, Luciana Canuti, Regina Motz, Nancy Peré, Alén Pérez Comisión Sectorial de Enseñanza, Instituto de Computación, Facultad de Ingeniería Universidad de la República Montevideo – Uruguay http://eva.universidad.edu.uy http://creativecommons.org/licenses/by-nc-nd/3.0/
We propose a categorization oflearning design developed withinthe framework of virtual educationalenvironments.It is aimed at the identification ofcategories and the constructionof learning design patterns forcourses listed in the Moodle platformof the Universidad de la República,Uruguay.
Moodle in UruguayFour years using Moodle as theeducational platform of the Universidad de laRepública, Uruguay.80.000 users (professors and students) ateva.universidad.edu.uyA great Moodle community with users inmost of the educational institutions andorganizations in UruguayOrganizing our 2nd MoodleMoot Uruguay2012 moodlemoot.org.uy #MoodleMootUY2012
Starting points:We are all in Moodle, and we want tohave good educational practicesGood learning design is one of thekeys for better educational processesLearning design patterns could beorganizers for teachers so as toimprove the ways they teach invirtual learning environments
How teachers design:We wanted to know how our bigcommunity of teachers aredesigning their classesDifficulties to identify the ways theyare designing, because our hugequantity and variety of courses inour plattform (more than 2500)
Virtual learning designVirtual design is defined as the application of agiven pedagogical model for a learningobjective, target group, context or knowledgedomain.A good design specifies teaching and learningprocesses, together with the conditions underwhich these are possible, including the activitiescarried out by professors and students withcertain results in mind within a specificframework. This framework is made out oflearning items and services used during theperformance of activities
Learning design categoriesFor the study we devised a categorization ofdegrees of utilization of the EVA by thedefinition of elements of learning design.It combines typologies found in literature andterminology used in Moodle.It was built based on increasing andacumulative degrees for the classification ofvirtual courses.Using this category, we classified the coursesbased on type and amount of activities, toolsand resources used:
Category 1:Courses defined as repository are those thathave resources that can be materials invarious formats such as text, images, videos, aswell as labels, directories, web pages. They havea forum for updates that comes built in whendeveloping a new course in Moodle. Thiscategory includes all courses with at least 4resources.
Category 2:Self-evaluation courses that are defined asare those that are repository and are centeredin the use of self-administered tools (i.e.:questionnaires, HotPotatoes, or consults).Courses with less than 2 activities fall in thiscategory.
Category 3:Participative courses are those that arerepositories, with self-administeredevaluation and have discussion forumsand/or have tasks. In this category a greaterlevel of interaction is required on behalf of thestudent. This category consists of courses thathave at least 4 of these activities.
Category 4:Collaborative courses are those that arerepositories, with self-administeredevaluation and have discussion forumsand/or have tasks, have activities such aswikies or glossaries, and are used withwebconference and/or chat resources. Thiscategory consists of courses that have at least 4of these activities.
Applying categoriesFor the study we improved the moduleStatistics in Moodle to gather data ondifferent types of resources and activitiesproposed in the courses, and their frequencyof use.The modified module shows detailed informationgenerating data from all courses of one category(including all subcategories). This was translatedinto Spanish and is available for free download
Course classification according to type andquantity of activities, tools and resources used
Results shows a strong concentration in theinitial stages of the categorization inclusionmodels.The preponderance of repository courses(39%) is strongly evidenced.Reduced percentage of collaborativecourses, especially if we consider all coursesthat include some type of collaborative activity.
Resource utilization profiles, according toknowledge areas. (Percentages for each area ofknowledge)
Utilization profiles are relatively similar amongthe various areas of knowledge.The area that uses more extensively the EVA as aninteractive space is Education (almost 70% ofcourses).Health Sciences follows participative repositorymodel in 60% of the courses. The weakness in thedevelopment of courses in these areas is in theinclusion of Evaluation and Self-Evaluation tools.The Scientific Technologic area stands out for theuse of the EVA as a repository (46%), as well asfor being the area that uses more tools for self-evaluation (9%).
Classification according to degree of utilization ofthe EVA and number of registered students.
The utilization of available tools in theEVA is more dependent on theteaching content than on theamount of students per course.It is clearly shown that courses thatmake more use of participative tools arethose with students ranging from 25 to60, an those that make more use of self-evaluation tools are the courses withmore students, 60 and above.
Conclusions and Future WorkValue of the categorization developed and of itsutility for the evaluation, monitoring andplanning of strategies for the improvementand further use of the EVA tools.
This methodology, supplemented by qualitativecase studies will allow us to move towards thedevelopment of a contextualized model,multidimensional and dynamic, previouslydefined from the comparison among:1. categories found in the practices of learningdesigns in virtual learning environments;2. Categorizations found in literature, developedby educational experts, defined for all cases as“good practices of learning design”
It would be possible to establish automaticrelations between the established model andspecific learning design practices developed fora particular course, based on the elaborationof patterns specific to the institutional context,developed using standard metadata.In the future it is possible to incorporate theanalysis of intrinsic characteristics of thedesign elements created as learning goals, aswell as relations among components and thespecific context (teaching styles by discipline,pedagogic models, among others) and/oradaptation to characteristics of studentprofiles.
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