Learning Activities in Personal Learning Environment

Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. Learning Activities in Personal Learning Environment Abstract: Nowadays Learning Management Systems are an integrated part of educational institutions. Teachers as well as learners profit from the so-called Web 2.0 applications in their daily learning process. Communication and collaboration between students have been enhanced using mashups of Web 2.0 technologies. Smart mobile phones and the increased availability of free wireless network access points make the integration of all these tools in our personal daily life and personal learning process much easier than before. This publication focuses on the Personal Learning Environment (PLE) that was launched at Graz University of Technology (TU Graz) in 2010. It illustrates how the PLE at TU Graz has been extended to move towards mobile PLE. Furthermore the learning activities of about more than 4000 learners in the last two years are revealed based on the tracked user behavior. The activities and usage traces are modeled using domain specific semantic ontologies. The models are used as the input for our Analytics Dashboard to visualize statistics and get a quick overview of learning habits and overall reflection usages and activity dynamics in the PLE. Introduction Tim O'Reilly (2006) introduced the word Web 2.0 the first time in 2006. Since then many online Web 2.0 applications and services have been raised like YouTube (for sharing Videos), Flickr (for sharing pictures), Slideshare (for sharing presentations), Scribd (for sharing documents), Mendeley (for sharing publications) or Delicious (for sharing bookmarks). The huge amount of such applications and their usage in learning and teaching has changed the online behavior and attitude of learners in respect to the new arising technologies (Downes, 2005). Many research studies have been carried out to observe and analyze how Web 2.0 applications auch as Weblogs (Farmer & Bartlett-Brag, 2005), Wikis (Augar et al., 2006), Podcasting (Towned, 2005) as well as Microblogging or Social Networks (Ebner & Maurer, 2008) influence users and enhance education. Mobile learning has gained more attention since the growth of smartphones and mobile application, driven by Apple’s iPhone and Android mobile Operating System. It was first surveyed in 2000 to see how the use of Personal Digital Assistants (PDAs) helps to increase the learning efforts (Kukulka-Halme & Traxler, 2005). Nowadays, especially in industrialized countries, many people are permanently online, share different resources and contribute to World Wide Web (WWW) with their mobile devices including teachers and learners in context of E-Learning (Ebner et al., 2008). Due to the fact that mobile technologies and social web are available ubiquitously and are pervasively used, they have influence on our every day life and learning environments (Holzinger et al., 2006, Klamma et al. 2007). On the other hand the WWW provides lots of different services; each can be used for teaching and learning. It is quite challenging for education not to be overwhelmed by all these various opportunities within a learning environment. Various studies on Web 2.0 usage amongst students (Ebner & Nagler 2010) underline that it is hard to follow these tools and even more to monitor them in an appropriate way. Mashups (Tuchinda et al., 2008) and personalization can be used to manage this challenge in learning environments. This led us to the idea of Personal Learning Environment (PLE), where tiny applications (widgets) can be integrated and combined within a learning environment managed by the learners according to their actual needs (Schaffert & Kalz, 2009). The PLE at Graz University of Technology (TU Graz) was first launched in October 2010. The main idea of using a PLE at TU Graz was to combine and integrate existing university services (Ebner et al., 2010) as well as resources and services on WWW in one platform in a personalized way (Ebner & Taraghi, 2010). It bases on mashup of widgets (Taraghi et al., 2009a, Taraghi et al., 2009b, Taraghi et al., 2009c) that represent the resources and services integrated from WWW within the PLE. The PLE has been redesigned in 2011, using metaphors such as apps and spaces for a better learner-centered application and higher attractiveness (Taraghi, 2012). The new User Interface (UI) relies on full widget or app-based architecture. It resembles pretty much to the mobile app environments, i.e. a widget store is offered where the learners can install widgets on one or many spaces or personal desktops. The resemblance of the new UI to mobile app stores attracted the users a lot. The statistics show an increased number of active users in average. Figure 1 illustrated a user’s spaces (space 1 in this case) where several widgets are installed and positioned by the user arbitrarily. Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. To support mobile learning in case of PLE, the mobile view of PLE has been developed as a step towards a ubiquitous PLE. This publication aims to introduce the mobile PLE and illustrate how the extension to mobile view can be carried out efficiently. Furthermore after two years of running PLE, concerning the increasing number of active users specially after supporting the PLE mobile, a tool is needed to reveal and analyze the users’ learning activities. In this publication we demonstrate how such a tool, in our case PLE Analytics Dashboard, can be developed and show some results of the first analysis regarding the learners’ activities, widgets’ usage and user’s behavior within PLE. Figure 1: One Space in PLE desktop view, full of widgets arranged arbitrarily by the user. Mobile PLE Considering the rapid growth of mobile devices and the increased availability of free wireless network access points, a new form of e-learning has been arisen, known as ubiquitous learning or u-learning. Learners have the possibility to use e-learning facilities any time, in any place and any situation. Zhan and Jin (2005) defined u-learning as a function of different parameters: u-Learning = {u-Environment, u-Contents, u-Behavior, u-Interface, u-Service} The mashup-based concept in PLE at TU Graz meets actually the parameters defined above. The learners have full control over the content in PLE (widgets), they decide how and what to work with, they have full control over the UI, number of spaces they need and distribution and positioning of widgets within spaces and finally the services they would like to use. The environment where they could access their PLE was reduced only to desktop devices. The mobile view of PLE was the next step towards ubiquitousness. The PLE at TU Graz bases on a Service Oriented Architecture (SOA). In such architecture the client side logic is apart from a server side backend. The frontend is responsible for the whole UI on the client device whereas the server side backend is responsible for the permanent data storage and data management. The client communicates with the server over a specified Application Programming Interface (API) to retrieve data needed on the client UI. To support mobile devices, only the client side logic had to be extended. The same is true for the existing widgets. The widgets needed to be refactored in a way that they run on mobile devices. The widget development framework that has been used for widget development at TU Graz (Taraghi & Ebner, 2010) relies on a well-known MVC design architecture. It reduced the refactoring time to a great extent as only the Views had to be refactored. The mobile interface is introduces briefly for the sake of completeness. Figure 2 (1) shows the start page of the mobile PLE after login. Users can navigate to different pages within PLE from this page. Figure 2 (2) shows a list of user’s spaces and the widgets that the user has already installed in each space. Selecting a widget would open up and start the corresponding widget immediately. Figure 2 (3) shows “Geolines” widget already opened up by the user on her mobile device. The “Geolines” widget solves direct and inverse geodetic Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. problems in 2d-cartesian and ellipsoidal coordinate systems. Users can trigger several actions on each specific widget. Clicking on the “option” button on the top right corner (see figure 2 (3)) opens up a dialog window where the user can select the desired action (see figure 2 (4)). Figure 3 demonstrates the widget store. On the left an overview of all provided widgets is offered. On the right the detail view of the widget “Geolines” is depicted. For information about the overall UI structure of the desktop PLE refer to (Taraghi, 2012). Figure 2: PLE mobile view, from the left: (1) Start page, (2) list of user’s spaces and widgets, (3) “Geolines” widget running, (4) list of widget actions. Figure 3: PLE mobile view, left: widget store, right: detail view of a widget in widget store. PLE Analytics Dashboard The PLE at TU Graz has been running since two years. In order to enhance PLE in general and improve the usability as well as usefulness of each individual widget a tracking module was implemented (Taraghi et al., 2011). The tracking module helps to track the user behavior and capture implicit information about what widgets and how often they are being used and in which activities users are mostly engaged. By the time there are more than 4000 active users in PLE. It is quite interesting to analyze the tracked data for the better understanding of common popular learning activities and PLE usage. To fulfill this goal a semantic based approach has been applied to enrich and model the data gained through the tracking module. The semantically enriched information are used in the next step as the input to an Analytics Dashboard to visualize the learners’ activities, widgets’ usage and user’s behavior. Figure 4 (left) demonstrates the main three dimensions of PLE measuring confidence that are taken into consideration for the analytics. Activities reflect user’s behavior. Statistics about widgets usage can be used for prediction and recommending of widgets to other users. The data about user’s behavior help to detect implicit information about the user that can be used later for a recommender system within PLE, i.e. user’s interests and main learning context of the user. Reflection of leaner’s activities, the widgets’ usage and the tracked users are directly in relation to each other, which implies that one influences the other and vice versa. Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. Figure 4: Left: dimensions of PLE measuring confidence by monitoring widgets, activities and users. Right: PLE Analytics Dashboard. Figure 4 (right) shows the PLE Analytics Dashboard. It contains graph visualizations on the modeled information. The UI is split in a summary, which displays several graphs of measures derived from the statistics to monitor the confidence and balance of the PLE. In the following sections the semantically enrichment of user behavior is briefly described and the results of the first analysis are shown in detail. Semantic Modeling in PLE Analytics One of the most visible trends in last decade on the Internet is the emergence of Open Educational Resources (OER). OER can be found in e.g. Massive Open Online Course (MOOCS), which have not been considered before. Wikis, Videos channels, Slide shares, chat rooms, Learning Hangouts, Online Tests and Survey platforms are some other examples where OERs are shared by users massively. E-learning has become more interactive and learners have been consuming OERs more intensively than before. Currently such systems produce mass of learners’ related data, which can be useful for e.g. modeling learning designs, evaluation of learning services, enhancing the learning process and interlinking the learning communities. From technical standpoint information about learning activities and used resources within a learning platform, especially in a PLE, can be easily tracked with current state of the art technologies. Still current online learning communities are isolated from one another. The main reason for this lack of interoperability is the fact that common standards for data interchange and interlinking still have to arise. Another emerging trend in recent decade is the Semantic Web, which could offer some solution to the problem stated before. Technology stack defined by Semantic Web is well defined. Applying the semantic technology to describe a learning context, as an example, can lead to an interlinked and semantically rich knowledge source, which can be used for analytics and enhanced learning process. Modeling, structuring and processing of the collected data derived from the learner’s behavior in the PLE plays a decisive role for the evaluation. Different works outlined the importance of tracking activity data in Learning Management Systems (LMS) as well as the appliance of semantic tools for learning process adoption (Santos et al., 2012, Siadaty et al., 2011, Jeremic et al., 2012). Current research EU project IntelLEO aims to: "explore supportive technologies for learning and knowledge building activities of learners in Intelligent Learning Extended Organisations (IntelLEO)". As part of these efforts an ontology framework has been defined for Semantic Web1. Emerging technologies like the Semantic Web along with RDF2 and SPARQL3 are used data on specific knowledge domains using adequate ontologies. Linked Data has been fairly successfully used to generate correct interpretation of web tables (Mulwad et al., 2010) and the DEPTHS environment demonstrates how a synergistic combination of social and semantic technologies and Linked Data advances the learning process in software engineering (Fancsali, 2011). Additionally the Semantic Web introduces a retrieval standard: SPARQL, which enables easily querying of semantically enriched data. Meanwhile, the Semantic 1 http://www.intelleo.eu/ (last visited 12.12.12) http://www.w3.org/RDF/ (last visited 12.12.12) 3 http://www.w3.org/TR/rdf-sparql-query/ (last visited 12.12.12) 2 Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. Web turned into a Web of "Linked Data". This achievement relies on establishing principles that support sharing of large datasets on the Web together using the defined technology stack. Technology stack is simple and based upon URIs, RDF, and SPARQL. The initiative has led to success and widespread Linked Data efforts that cumulated in huge amounts of public data such as DBPedia4, WordNet RDF5 or similar. The Linked Data movement also supports the exposure of large amounts of reusable data and resources into the so-called Linked Data Cloud, a network of interlinked Linked Data sets6. The nature of data involved is ranging from domain specific expert knowledge up to data about cultural heritage like the Europeana7 dataset. Recently, the notion about these approaches is getting more adopted and accepted by education institutions. Within these realm Linked Data technologies is being used to expose public information regarding: course offering, educational resources and facilities. This has lead to the creation of a sub initiative named "Web of Educational Data" including institutions such as the Open University (UK) or the National Research Council (Italy), as well as Linked Data about publicly available educational resources, such as the mEducator Linked Educational Resources8. However, although the Linked Data approach clearly offers promising solutions for e-learning and online education, its adoption still waits for wide acceptance by the e-learning community. In this work we aimed to visualize the three different PLE dimensions of measuring confidence for optimization the PLE: User centric view (relations between learners and learning activities), activity centric view (activities bound to the widgets that a specific user has installed) and widget centric (reflection on widget usage among users and over time). The unstructured tracked data from PLE reflects the users’ behavior (anonymously) and includes information such as the widgets used, activities related to the widgets and timestamps of the user activities. In order to provide flexible data model that delivers all wide accepted formats such as XML or JSON as final output more operable and flexible data modeling framework and standards are required for maintenance of tracking data. Furthermore the data model should be extensible and scalable. It should be enriched with the context reflection, in which such data was collected. Since Semantic Web offers flexible and scalable approach to modeling, formatting data in this way was the next logical step. SPARQL is used as data retrieval technology from the created model. SPARQL frameworks support XML, JSON and common standard data outputs, which is a requirement in our approach. As mentioned before one of the main goals of IntellLEO EU project is building an innovative ontological framework for learning representation, which includes learners, context and collaboration models, serving to achieve the targeted synergy. In the realm of the IntellLEO project inside the provided ontology framework two special ontologies are well known. The first is the Activity Ontology, which offers a vocabulary to represent different activities and events related to them inside of a learning environment with possibility to describe and reference the environment (in this case PLE) where these activities occur. The second is the Learning Context Ontology, which describes the context of a learning situation. These two ontologies have been selected as adequate modeling vocabularies for the PLE tracked data as they comply very well with our use cases. Once the semantic models are created, SPARQL is used to process and query the data for visualizations in Analytics Dashboard. A SPARQL endpoint is provided as an interface for data retrieve. PLE Analytics Results In this section some results from the PLE Analytics Dashboard are demonstrated. Each dimension of the PLE measuring confidence is considered separately. The visualizations refer to the log data of the last two years from PLE with more than 4000 active users. Each widget in PLE is associated to one or more activities. As an example “Twitter” widget is associated to the activities “Reading”, “ContentSharing”, “DiscussAsynchronouly”, “Viewing” and “Search”. The other activities in PLE are “Authoring”, “Learning”, “Game”, “Quizzing”, “Computing” and “Listening”. According to the output of Analytics Dashboard, most learners are engaged in the activity “Reading” (4290 users) followed by “Authoring” (2461 users) and “Search” (2156 users). In contrast “Listening”, “Computing” 4 http://dbpedia.org/ / (last visited 12.12.12) http://www.w3.org/TR/wordnet-rdf/ (last visited 12.12.12) 6 http://richard.cyganiak.de/2007/10/lod/ (last visited 12.12.12) 7 http://www.europeana.eu/portal/ (last visited 12.12.12) 8 http://www.meducator.net/ (last visited 12.12.12) 5 Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. and “Quizzing” are not popular activities at all. Figure 5 (left) shows these proportions in user centric view graphically. Considering the activity centric view, quite the same results can be observed. Figure 5 (right) shows the distribution of user activity frequencies over all activities. The activities “Reading” (28406 times), “Search” (10588 times) and “Authoring” (9437 times) are the most popular activities in PLE. Again “Listening”, “Computing” and “Quizzing” activities are bottom-placed in the list. Figure 5: Left: dimensions of users over activities. Right: distribution of activity frequencies over all activities The widget centric view offers progressions of widget usage over time. The visualizations show e.g. what widgets and how often they have been used by a specific user over time period. The administrator can monitor the activities of each learner in detail considering the widget centric view. Figure 6 (left) shows the overall frequencies of all widgets usage in PLE by all users in the last two years. The most frequently used widgets are as follows: “ZID News” (representing the actual news related to the Central Informatics Service), “TUGraz online” (representing Administration System at TU Graz), “TUGraz Newsgroups” (News groups), “TUGMail” (official E-Mail service of TU Graz) and “TeachCenter Courses” (LMS platform at TU Graz). All these widgets represent services that students use daily at TU Graz. The highest range of user activity can be monitored from October (begin of the winter semester) until July (end of the summer semester). Expectedly on the first week of January as well as in summer holidays no active usage cam be seen in PLE. This visualization helps to detect widgets that are not popular at all or have been rarely used over the whole monitored period of time. Widgets “Google Search”, “Address Book”, “Plane-Sweep Algorithmus” and “laengste gemeinsame Teilfolge” (a learning object to support learning an algorithm) are such examples that must be revised in a further development process. No remarkable change can be observed for unpopular widgets over time regarding the frequency of usage. Figure 6 (right) depicts the distribution of activities from activity centric view over the two years period in PLE. It can be interpreted as the overall activity intensity in PLE. The results revealed in this visualization confirm the other results from activity centric view. “Reading”, “Authoring” and “Search” activities are clearly dominant, especially in year 2012 in comparison with year 2011. In this visualization the progression of activities, in which the learners are engaged during time (2011 and 2012) is clearly observable. Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. Figure 6: Left: frequencies of widgets usage by all users over time. Right: distribution of activities over time. Monitoring the activities of each specific learner over time can be of high interest in any e-learning system. We demonstrate here two examples from widget centric view. Figure 7 (left) is an example of an active user who has tried many widgets but has been using only three widgets steadily since February 2012 (“KulturKalender Graz”, “ZID News” and “TUGMail”). As it can be seen in the visualization she has stopped using “TUGMail” from April to August 2012. Figure 7 (right) depicts the activity of another sample user who has been using only two widgets: “ZID News” widget continuously and “TUGraz Newsgroup” widget in some points of time, probably whenever she has needed to write some threads in the news groups at TU Graz. Figure 7: Distribution of usage of widgets by two different sample active users in PLE. Conclusions In this publication we introduced at first the mobile view of PLE at TU Graz as the first step towards ubiquitous PLE. We showed that according to the SOA structure of PLE and the MVC design architecture that is used as the basis for widget development it is quite less time consuming to extend the whole system to support mobile devices. Originally published in: Taraghi, B., Softic, S., Ebner, M. & De Vocht, L. (2013). Learning Activities in Personal Learning Environment. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2013 (pp. 2466-2475). Chesapeake, VA: AACE. The redesign of PLE and its resemblance to mobile app environments as well as the mobile View led to an increase of attractiveness of PLE and consequently a higher number of active users. Then we introduced the PLE Analytics Dashboard as a tool to monitor and observe the learning activities of users and overall usage of widgets in PLE based on the tracked data of user behavior. The progression of these activities over time can be observed as well for each user individually. We categorized the analytics results into three dimensions of PLE measuring confidence, namely user centric, widget centric and activity centric views. We showed how the tracked data are modeled semantically, using common standards from semantic Web, and prepared to be used in Analytics Dashboard. The advantages of Semantic Web technologies combined with the adopted vocabularies and ontologies do not only support easy and flexible analysis, it extends the repositories to the outside world while implementing implicitly many interoperability options for external analytic systems. The Analytics visualization helps us to gain deep insight into the behavior of a single user in a certain period of time. We showed examples what we have achieved with a PLE Analytics Dashboard. The examples covered the user centric, widget centric and activity centric dimensions of the PLE confidence model we introduced. The overview over distribution of activities can reflect the overall interest of the learners within PLE. It can be concluded that in case of our PLE users are more consumers than contributors. Activities such as “Quizzing” and “Learning” (supported by some learning object widgets) are not quite popular. Our investigation showed that the corresponding widgets that support those activities must be revised in regard to some usability issues. We can obtain a kind of rating/quality measure for the widgets that can be used as an indicator of likely future activity in the PLE. Distribution of usage of widgets over time in PLE showed exactly which widgets have been popular in certain period of time. Widget centric results for a specific user reflect widgets that are favored by a single user: We can observe if this trend is trackable over time or not. It can be used e.g. as a basis for recommendation of new widgets in the widget store within PLE. Through activity centric statistics we gain a better insight in the activities done in the PLE and their use. We can get insight about dominant activities, activity dissemination over time and activity peak usage periods. It is planned in near future to apply PLE Analytics Dashboard for some specific courses at university. The goal is to analyze the learner’s behavior in detail, the widgets they use or stop using for the given learning goals and map their monitored actions to their learning results. The main question will be how or if the PLE Analytics Dashboard must be further improved to meet these goals. The examples demonstrated for now show that it would be possible. We will do this survey as the next step in the near future. References Augar, N., Raitman, R. & Zhou, W. (2004) Teaching and learning online with wikis. In: Atkinson, R., McBeath, C., Jonas-Dwyer, D. & Phillips, R. (eds.) Beyond the comfort zone: In: Proceedings of the 21st ASCILITE Conference, December 5-8, 2004, pp. 95--104. Perth, Australia Downes, S. (2005) E-learning 2.0. ACM e-Learn Magazine, 10 Ebner, M. & Maurer, H. (2008) Can Microblogs and Weblogs change traditional scientific writing?. In: Proceedings of E-Learn 2008, pp. 768--776. Las Vegas, (2008) Ebner, M. & Nagler, W. (2010) Has Web2.0 Reached the Educated Top?. 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