Int. J. Social Media and Interactive Learning Environments, Vol. 1, No. 1, 2013
Social learning graphs: combining social network graphs and analytics to represent learning experiences Abelardo Pardo School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia E-mail:
[email protected] Abstract: The web is evolving towards higher levels of personalisation, and closer interaction through social networking sites. The wealth of available data about user interactions has prompted the appearance of highly personalised tools for social interaction. Learning experiences are following a parallel evolution, and they need more effective personalisation and a strong social component. Semantic web was presented as the solution to achieve a highly personalised learning experience. Analogously, social networks are used to represent relations among students. But a simple and common representation of these two perspectives in the context of a learning environment is missing. In this paper, Social Learning Graphs are presented as a framework to capture and represent the interactions and relations occurring among multiple entities in a learning environment. The advantage of this representation is that it combines structural in formation with observations in a common graph notation suitable to be used by procedures such as link-prediction, recommendation, and abstractions. Keywords: social networks; semantic web; intelligent leaning; environments; learning analytics. Reference to this paper should be made as follows: Pardo, A. (2013) ‘Social learning graphs: combining social network graphs and analytics to represent learning experiences’, Int. J. Social Media and Interactive Learning Environments, Vol. 1, No. 1, pp.43–58. Biographical notes: Abelardo Pardo is a Lecturer at the University of Sydney, School of Electrical and Information Engineering. He holds a PhD in Computer Science from the University of Colorado at Boulder. His research interests are in the area of technology enhanced learning with emphasis on learning and behavioural analytics, computer supported collaborative learning, and personalisation of learning experiences. He has participated in national and international projects funded by NSF and the European Union. He is the author of numerous publications in prestigious conferences and journals, member of the steering committee of the Society for Learning Analytics Research (http://www.solaresearch.org), and member of the editorial board of the Journal of Social Media and Interactive Learning Environments.
Copyright © 2013 Inderscience Enterprises Ltd.
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Introduction
We are living in the era of personalisation in the web. Technology allows every user to access a different version of available resources personalised based on aspects such as gender, age, location, time of day, affinities, etc. This trend rests on a combination of various aspects that together offer a fertile ground for innovation. First, the use of digital devices is increasing specially in the so-called digital natives. In the 2012 London Olympic Games, nearly 160 million videos were streamed over a total of 9.9 million digital devices (Dachman, 2012). Second, these digital devices all have the capacity to capture and record user interactions and relay that information to servers to produce the so-called big-data. These data sets are so large and complex that specific information technologies are needed to manage them. The purpose of this manipulation is to deduct how to better adaptor personalise content for each user. This trend once confined to the web is now being applied to various disciplines and learning is at the forefront of this wave. Even before technology had such a large presence in learning experiences, personalisation was acknowledged as having the potential to improve learning gains. With the advent of technology and its increasing presence in multiple aspects of a learning experience, there has been an increase in hype about how learning, or even education in general, could be significantly improved by personalisation through the use of technology. The term Intelligent Learning Environments (ILEs) has been used to denote those applications that leverage current technological advances to offer environments in which learning occurs more efficiently. Over the years, the increase of available resources has been parallel to an increase on the complexity of its management, and the need for a more structured content suitable to be understood not only by humans but also machines prompted the appearance of the semantic web. Learning in general and ILEs in particular have also been influenced by this trend. After all, a significant part of a learning experience amounts to gathering, organising and sharing the knowledge in a specific domain. The objective of the semantic web when applied to learning is to obtain a structured representation of knowledge. In theory, if the information available in the web is properly tagged and an adequate categorisation (called ontologies) is produced, systems in general, and learning systems in particular, among other advantages, would be much more effective at selecting the most relevant resources from the network thus contributing to a better personalisation. In parallel with personalisation and the semantic web, technology also brought another aspect closely related to learning: social networks. In its infancy, the web only offered the possibility of accessing information. Today, in what is known as the web 2.0, users interact with each other, create their own resources, share them with peers, friends, etc. Users, and not business, are now at the centre of the web experience. The appearance of social networking platforms allows users to group around interests, share documents, opinions, pictures, comments, interests, etc. Learning also has a strong connection with social networks. Some authors claim that the future of education is networked, and as such, heavily influenced by these technologies (Cisco Systems Inc., 2010). In fact, social learning theories have been proposed even before the advent of social networks in the web stating that learning takes place in a social context where students can observe and model the behaviour they see around them. The evolution of these social networks has been the study of the area called Social Network Analysis (SNA). A variety of computational techniques have been proposed to represent, analyse and then predict how these networks evolve overtime. The initial focus
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of these techniques was on how users relate to each other. As in the case of the previous trends, SNA can be used to represent learning communities and analyse how the community of students and instructors is created and how their relationships evolve over time. In recent years, there have been an increasing number of scientific publications that apply these techniques to analyse the networks emerging with in a learning environment. The information derived is typically used to provide self-awareness and reflection. But this analysis is mainly based on how users are related to each other. The next step on how these networks can be used is clearly illustrated by the recent announcement of Facebook to allow third party applications to relay user interactions to be included in the graph representing their social network (Brown, 2011). Facebook used to record and show when users liked certain resources, but with these enhancements, the interaction with other applications outside Facebook can be automatically aggregated to the social platform. This enhancement has a profound effect on how the information is collected, managed and used for predictions. Social networks are being extended to capture not only relationships among users, but also to include interactions between users and resources. Interactions among students, between students and instructors, or between both of them and course resources with in a community have also been identified as an essential part of how humans learn. Especially in situations in which learning by doing techniques are used, interactions are at the centre of the process to acquire knowledge. Traditional assessment techniques used to verify the effectiveness of a learning experience by analysing the outcome of the process through exams, quizzes or similar methods. In fact, a much better assessment is possible by focusing on the process instead of its final consequence. Learning analytics and educational data mining are two emerging disciplines that are centred in this aspect. Detailed information about how the different stakeholders in a learning experience interact with each other and the available resources can be collected and analysed. The result of this analysis can range from useful visualisations to support self-awareness and reflection, to the deployment of specifications within the environment. There are numerous learning analytic initiatives and commercial tools that are having an impact in the educational market. The idea proposed in this paper is based on the combination of the following perspectives: 1
digital devices offer detailed recordings of how learners interact
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learning analytic techniques allow the manipulation and analysis of the recorded events
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semantic web technique sallow structure and knowledge to be represented for a specific domain
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social networks offer an efficient representation of learner relationships from which predictions can be calculated.
The concept of Social Learning Graph (SLG) is proposed as a representation framework to capture in one single structure the relations emerging within a social network in a learning community, the basic structure of the domain of knowledge, and the events derived from the interactions occurring in the learning environment.
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The objective of SLGs is to combine the exhaustive account of events provided by learning analytics, the knowledge representation offered by the semantic web, and the power of prediction of how relationships evolve in a social network to represent a learning environment and offer immediate feedback to all stake holders based on its evolution in time. The rest of the paper is organised as follows. Section 2 describes how ILEs are used to personalise learning experiences and the models they use to represent users and knowledge. Section 3 explains how semantic web and social networking technologies have been applied in the context of learning. Section 4 describes how learning analytics is emerging as a paradigm to manipulate exhaustive observations of the events occurring in a learning environment. Section 5 introduces the definition of SLGs, shows how the interactions occurring in a learning environment among students, instructors and resources can be captured and outlines the type of operations that can be applied to the representation. Finally, Section 6 summarises the main conclusions of the paper.
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Intelligent learning environments
The concept of ILE is typically used to encompass systems known as Adaptive Educational Hypermedia Systems (AEHS) or Intelligent Tutoring Systems (ITS). The objective of these applications is to provide students with personalised interactions with resources (feedback, hints, etc.) as well as with other actors participating in a learning experience based on tracking how students work to increase learning gains. These interactions are selected and offered depending on a wide variety of factors such as personal traits, the learning environment, the expected outcomes, current achievements, etc. The architecture of these applications is comprised of an expert module containing the representation of knowledge in a specific domain, a pedagogical module representing different pedagogical strategies, a student module containing a set of collected features, and an interface module to interact with the user (Wenger, 1987). There has been a significant body of research studying how to adapt learning content and curricula to students in the last years (Brusilovsky, 1996), and there has been an equally varied set of applications as well. Initially they were stand alone applications restricted to one single content model. Examples of these systems were ELM-ART (Weber and Brusilovsky, 2001) or AHA! (De Bra et al., 2003). Later applications extend the functionality by offering more advanced adaptation and the use of different content and adaptation models (Brusilovsky, 2001, 2004; Henze and Nejdl, 2000). The main limitation of these systems is that they tend to focus on individual learning in a very specific domain of knowledge. The system decides the adaptation based on data collected from users through tests and forms, and the events observed within the application itself. But the current pattern of interaction in learning is evolving towards the use of multiple loosely coupled applications in different devices. Also, social interaction with peers within multiple communities beyond the control of an instructor is increasingly common, the range of possible pedagogical practices includes new scenarios that were unfeasible in the past, the number and type of resources available is enormous, and finally, the variety of possible environments in which learning takes place is increasing accordingly. The current picture is one of students that interact very frequently with a large number of devices (Prensky, 2001) and therefore have more difficulties with learning strategies that exclude these devices.
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Conventional ILEs are coping with this new scenario by increasing their capacity to capture and represent knowledge, but in this effort numerous issues appear that quickly raise the barrier for massive adoption. A traditional ILE is as powerful as its models, and creating such models is a costly task requiring special skills from the instructor. An alternative solution would be a knowledge representation that captures its full semantics, simple to create, deploy and maintain, and incompliance with current web and learning technologies. The key observation is that a significant part of the valuable information used or produced in a learning environment is derived from the interactions. In other words, knowledge per-se is only a fraction (alas an essential one) of the entire ecosystem. Interactions among students, students with instructors, and both types of users with content offer an equally important and valuable pool of information. This trend is confirmed by the evolution of current social networking sites such as Facebook, Twitter, Linked In, or Google+, to mention just a few. But these communities tend to have too wide scopes which translate in less effective user customisation (Becker, 2011). Users in these networks have one single place in which they combine relationships from multiple contexts. A really valuable comment in a very specific topic can be easily buried among the rest of interactions. But learning communities avoid the problem of multiple contexts by construction. A set of students and instructors in a learning environment is a highly focused community with a very concrete set of goals and interests. The representation proposed in this paper is based on the observation that a basic knowledge organisation can be combined with a detailed representation of how the users in a highly focused community interact and collaborate. The objective of ILEs can be fulfilled but focusing more on how the interaction among all entities in the community evolve instead of trying to capture a detailed model of the knowledge.
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Semantic web and social networks in learning experiences
Back at the turn of the century, the amount of information available on the web was enormous and at the same time, weakly structured and only understandable by humans. The need for machines to understand and process information was perceived as a much needed development, and from this need the concept of semantic web appeared (Lee et al., 2001). The objective is to extend content with the required information to be processed by computers and then use intelligent techniques to take advantage of these representations (Antoniou and Harmelen, 2004). By leveraging in this enhanced content, services would be able to select the most relevant content for a user. The mechanism to describe and capture content knowledge is the ontologies. Ontology can be seen as a categorisation of a specific domain of knowledge and consists of a list of terms and the relationships among them. A web in which resources contain information about the terms they relate, and these terms are themselves related to other terms by means of ontologies is ideal for machines to quickly process large number of documents and select the most relevant ones. The resource description framework (Klyne et al., 2004) was proposed to describe these terms and relationships. Languages such as OWL were also proposed to describe, publish and share these ontologies (Bechhofer and Harmelen, 2004).
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Semantic web techniques have also been applied to learning experiences (Verbert and Duval, 2004). As stated in the previous section, modelling knowledge and organising resources is an important part of any learning environment, and as such, the potential for improvement seemed feasible. Tools such as LOCO-Analyst are an excellent example of how ontologies can be used effectively in a learning context. The system uses ontologies to organise the knowledge in a learning context and combines this information with observations obtained from the environment to back-annotate and report valuable information to instructors (Jovanović et al., 2007, 2008; Jeremić et al., 2009; Gašević et al., 2007). But the adoption of semantic web techniques has been uneven and not as widely used as anticipated. The main barriers were the creation and maintenance of the underlying ontologies and the required annotation of resources. A similar situation occurs with respect to learning. Currently, only a small set of domain offer ontologies and semantic annotations (Tiropanis and Davis, 2009). The concept of social web or web 2.0 emerged in parallel with the semantic web. In this new view, the network is a platform that connects users through a variety of devices, where software is available and continuously updated, and data a reproduced, consumed and remixed through user participation (O’Reilly, 2007). This new trend has brought a new set of functionalities to mainstream. Any user now can create content collaboratively, interact with friends, share pictures, write comments in blogs and wikis, exchange short messages, etc. As a consequence of this new environment, an attempt to capture these relationships among users appeared. Social networking platforms such as Facebook, Linked In or Google+ appeared to allow users to connect with friends and colleagues. Social networks have been portrayed as a set of connections. In fact, there are numerous analysis of the benefits derived from networks and most of them refer to the possibility of connecting to other users that themselves have other connections. But much less attention was devoted to the power of sharing through those connections our interactions with other objects. After acknowledging the power of having numerous connections, social networks turned their attention to sharing activities through those links. Facebook first all owed users to send gifts within the platform, and then introduced the ‘I like it’ button. Similar platforms such as Digg (http://www.digg.com) base their value on capturing how users interact with resources and combine this information with personal relationships. Social capital is defined as the network of ties and common resources from which people can derive value (Huysman and Wulf, 2004). In the context of information technology, social capital is related to the value that users obtain from networking sites. Well known SNA techniques can be applied to analyse the relationships among social entities in the network and obtain estimations of their social capital (Wasserman and Faust, 1994). The networks are usually represented using three formulations: graphs, sociometric representation, or algebraic notation. Graphs area suitable representation because they provide an intuitive terminology, a well-known set of mathematical operations to be applied, and theorems that can be tied to the notion of social value. A social network is defined as a graph G =〈V, E〉, where V is the set of nodes, and E is the set of edges, arcs or lines. For every edge e ∈ E, e = 〈u, v〉 where u, v ∈ V. Each edge in the graph represents an interaction between the two nodes that connects at a specific time t. Multiple interactions between two nodes can be stored as parallel edges among two nodes. A subgraph G’ = 〈V’, E’〉 from graph G is defined if G’ ⊆ G and
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V’ ⊆ V. Given a subset of the edges in a given graph, the edge-induced subgraph is the subgraph containing exactly these edges, and only the nodes connected by them. Analogously, given a subset of the nodes in a graph, the node-induced subgraph is the subgraph containing exactly these nodes, and only those edges connecting two nodes in the subset. Social networks are highly dynamic and changing very quickly overtime (Liben-Nowell and Kleinberg, 2007). Understanding this evolution is fundamental to gain insight in the underlying processes shaping it. All edges in a social graph contain the time in which they were created. Given two times t1 < t2, G[t1, t2] represents the edge-induced subgraph derived from the edges with time values t1 < t < t2. This formalism is ideal to capture the evolution of a social network overtime. Typical measurements in these networks are local and global clustering coefficients measuring the probability that nodes with common neighbours are connected, and measuring if a node is typically lies between other nodes in the network, largest connected components, diameter, average path length, etc. Additionally, some other techniques are used to predict the evolution of a network overtime. The link-prediction problem is stated as follows: given a snapshot of a social network at a particular point in time, can the appearance of future connections among its nodes be inferred (Liben-Nowell and Kleinberg, 2007)? This problem is used to predict the appearance of new relations in a social network. For example, in a graph representing the social network of Facebook users in Iceland, these techniques were used to infer future relations among these users (Backstrom, 2011). A large variety of computational methods have emerged to obtain robust results. A detailed survey can be found in Lü and Zhou (2011). The SLGs proposed in this paper can be seen as an extension of social network graphs. If the community under consideration is a learning community, the relation among users can be complemented with two additional perspectives: the resources, and the interactions. These two types of entities can be included as nodes in the graph. The first one can be simply as a description of additional nodes, and the second as the inclusion of annotated relations among nodes (students, instructors and resources). In this extended representation, techniques such as link-prediction can be applied not only to infer future connections among users, but also future interactions among all nodes.
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Learning analytics and big data
The widespread use of technology allows people to capture a detailed set of the events occurring in learning scenarios. The analysis of the data has been the focus of emerging research disciplines such as academic analytics (Goldstein and Katz, 2005), educational data mining (Baker and Yacef, 2009) or learning analytics (Long and Siemens, 2011). The collected data can be used for multiple tasks ranging from simple feedback to instructors only, self-awareness and reflection for students, or automatic adaptation of the learning environment. In recent years there has been a surge in the effort to track student events in different learning scenarios (Ferguson, 2012). Most of these initiatives rely on the data captured when students use a Learning Management System (LMS). For example, the signals project developed at Purdue University analyses the data collected in the LMS to predict students that are in danger of falling behind in a course (Tanes
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et al., 2011). Macfadyen and Dawson (2010) analysed the different factors derived from the logs in an LMS and selected the most appropriate to anticipate student performance. Other examples of analysing behaviour are forum discussions, where messages are analysed to infer leading or passive students, the type of messages, etc. (Lin et al., 2009). Other more exhaustive approaches provide feedback to instructors derived from multiple modules within the LMS (Jovanović etal., 2007). The connection between analytics in learning environments and ILEs is direct. A system observing how students interact in a learning environment and using that information to infer changes is clearly a form of adaptation. The difference is that Learning Analytics emphasises capturing data in a generic context and derive structure from the data, instead of relying in a model of the domain of knowledge. But as acknowledged by various authors, data derived from LMSs offer a serious limitation to observe a learning environment (Dawson et al., 2008; Romero Zaldívar et al., 2012). More comprehensive approaches propose the use of monitoring techniques applied to generic tools instead of LMSs (Blikstein, 2011; Pardo and Delgado Kloos, 2011). As a result, the collected data offer a more precise view of the events occurring in the learning environment, but at the same time require an efficient representation due to its size. Some initiatives already exist to capture, describe and exchange information about how users distribute attention while working (Schmitz et al., 2007; Wolpers et al., 2007) but these representations do not take into account the social relationships among users. In this paper, a representation is proposed to capture both the events occurring in a learning environment and the relationships among its actors.
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Social learning graphs
The proposed formalism to capture the essence of a learning environment is derived from the combination of various well established paradigms and perspectives. First, social networks and the relationships among its members are already efficiently captured with social network graphs. SNA techniques are then used to analyse the evolution of these entities over time and infer its future structure. Second, analytic applications are being used in learning environments to obtain detailed representations of events overtime. The analysis of these events allows systems to adapt various aspects of the learning environment to maximise learning gains. Third, knowledge in learning experiences tends to have a simple structure easy to capture. Courses are organised around topics, lessons, modules, etc. This structure can also be used to detect the most adequate interactions that could occur in the future. And fourth, students in a social framework may benefit from observing how the community evolves overtime through the multiple interactions that take place among students, students with instructors, or both of them with resources. The underlying motivation to propose the SLGs is derived from the observation made by Anderson (2003) that the different teaching and learning strategies can be captured when analysing their modes of interaction. Aside from the usual interactions between students and instructors, Anderson extends this taxonomy to students among themselves, both students and instructors with course content, and even content with itself. We believe that this categorisation captures the dichotomy previously explained of detailed account of student interaction with content, and social relationships emerging in a community of learning. A similar connection was explained in detail by Jovanovic et al.
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(2009). They argue that semantic web and social web can be combined into Social Semantic Web (Gruber, 2008) and adopted by AEHSs. SLGs follow a similar approach but are more slanted towards representing user interaction in detail and incorporate simple knowledge structures easily derived from currently existing sources, and thus, without the need for ontologies. The claim is that social relationships and detailed account of interactions in a learning environment considered separately offer certain advantages, but there is untapped potential to predict the evolution of a learning environment when both are combined into a single representation. The recent announcement of Facebook opening its social graph so that external applications can automatically upload new relationships derived from user interactions is a clear step in the direction of unifying social and interaction data (Brown, 2011). But the benefits of this unification can be amplified in a learning community. When interacting in a learning community, users have a concrete purpose and context, and therefore need more precise suggestions. A generic social networking platform may suggest new users to connect, links to explore or activities to undertake but related to your entire universe of connections. A SLG can be used to perform the same analysis but in a much more restricted context, and therefore deduct more targeted suggestions. A SLG is defined also as a graph G = 〈V, E〉, where V is the set of nodes, and E is the set of edges. Nodes in the graph are now extended to represent any entity in a learning environment. This includes persons (students, instructors, administrators), resources (documents, multimedia resources), sets of resources (lessons, modules, courses), tools (editor, oscilloscope, submission processing system, discussion forum, etc.), or even spaces (classroom, discussion room, laboratory, etc.). Each node has an attribute stating its type from the previous categories. Nodes may have an arbitrary collection of attributes represented by pairs (name, value). Additionally, each node may include a collection of node references. This addition allows a graph to accommodate multi-nodal structures such as, for example, groups of students. Edges in the graph are extended from their counter parts in social graphs to represent generic relations among nodes. These categories are distinguished by a type attached to each edge. The graph is in fact a directed multigraph. The source and destination of the edge is relevant, and various edges can connect the same pair of nodes. As in the case of the scheme proposed by Open Graph (http://developers.facebook.com/docs/opengraph), edges have a start-time and end- time attribute denoting its validity. These attributes allow capturing the evolution in time of the network. Edges with no end-time attribute are considered permanent and can be used to represent facts such as scores obtained in a previous course. Additionally, as in the case of the nodes, edges contain also a collection of attributes represented as pairs (name, value). The attribute tables in nodes and edges have been included to provide a generic frame work for tagging. Instead of relying on totally formalised paradigms (for example, through ontologies), the graph contains an open labelling policy so that it can be adapted to different learning experiences. This decision prioritises flexibility to represent different scenarios (any relation can be captured) at the expense of specialising the graphs only to one learning experience (a graph from one learning scenario will be difficult to reuse in a second scenario). A more formal and exhaustive description of the attributes required for each type of node could be produced, but the recent history of information technology is rich in examples of such efforts that end up as huge over-engineered paradigms with lower than expected adoption rates.
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The novelty of the proposed framework is not in its formulation, but in the type of information that can be represented. Under the previous definition, a set of tuples (subject, action, and object) similar to those used to reason about content in semantic web applications is feasible. Also, the relationship between actors in a learning community is captured by SLGs with the same level of detail that other representations currently used. The advantage of the proposed graphs stems from the combination of social and structural knowledge under a unifying formalism. By combining information about personal relationships with interactions, new relationships of various types are now feasible. For example, users interacting with a tool and repeatedly accessing a resource within a similar time window might suggest the relevance of the document when using the application. This relation can be encoded in a per-author basis, thus capturing potential differences among students when using course resources. By combining the social relationships, resources or tools could be recommended based on both structural and social affinity.
5.1 Representation of learning activities The best way to perceive the expressive power of SLGs is showing how specific situations in a learning environment can be captured. Figure 1
Students exchange feedback about tool use
Figure 1 shows a SLG capturing the interaction between three students, a tool and a discussion forum. Only the relevant information attached to nodes and edges is shown for the sake of simplicity. Edges show the type of interaction and its time order (if relevant). Initially, students U1 and U3 both belong to the same group. All three entities are represented by nodes. It can be assumed that the top right node representing the course forum also exists. The first interaction that takes places is user U1. Using the tool, represented by the edge with label use (1). After a short time interval the student has some questions and resorts to posting a message in the course forum. With this interaction a new node M1 appears, as well as a new thread. These events can be easily detected in the LMS and encoded with the appropriate edges and nodes. Next, student U2 reads the message just posted, represented by the edge with label read (3) and answers with message M2. As in the previous case, both messages are incorporated to the SLG with edges pointing to the same thread in the course forum. The next event that occurs is user U3 using the tool previously used by U1. But users U1 and U3 are socially related though a group membership. Using link-prediction techniques, the edge connecting user U3 with the thread containing the two posted
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messages could be predicted. The effect of this prediction could have various possible effects in the environment: insert the title of the thread in the user space, send the discussion through e-mail, notify the instructors, etc. This example shows the benefit of representing in a SLG events (tools being used, post in a forum, message read), together with course structural information (a message is part of a thread in the course forum). In this case, a plausible relation to infer is that between a user and a forum thread. The technology to infer new connections in a social network is already being used to suggest new friends. In this case the concept is generalised to new interactions with any entity in the network. Figure 2 shows another example in which structural and social information can be combined in a SLG to deduct new relationships among entities. As in the previous example, only the minimum set of labels are shown for the sake of readability. Let us assume that a course contains a topic that needs to be discussed in an off-class session where students work in pairs with the help of the supporting document D1. The events occur in the following sequence. First, the students in the left side of the figure meet and discuss about the document. During this process they browse the net and find document D2. This event is recorded, and after the appropriate analysis (the content is close to D1), it is ruled as related to the topic and thus reflected in the graph. At a later time, two different students (represented in the right part of the graph) meet to discuss the same supporting document. The interaction of these students with the newly discovered resource could be inferred. In this example, three relationships are inferred. The first one is not having an immediate effect in any of the students. However, the relationship between the two documents is relevant to the learning environment because the interaction of two students before prompted this connection. Figure 2
New relevant document
There are numerous additional examples that can be shown to illustrate how SLGs capture the power of combining information from different sources in a common formalism, but they all can be derived from a comprehensive representation of how the learning environment evolves over time and how to infer its future structure from the given data.
5.2 Operations over Social Learning Graphs A representation as the one proposed in this document is as good as the operations that can be performed over it. In the previous section two examples were presented to
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show how the information is captured by SLGs but also how procedures such as link-prediction algorithms can be used to infer new relationships among the entities. This is probably one of the most powerful methods to derive adaptations of the learning environments. By generalising the node representation to include entities such as resources and tools, and the edge to capture a wider set of relations, link prediction translates almost into direct recommendations. These recommendations can be relayed to the environment in different flavours. For example, the time students devote to browse the net searching for additional resources that helps them tackle a task or understand a concept (Romero Zaldívar et al., 2011) can be detected and either included in the student space, relayed to instructors for approval before being published, or simply shown in the personal profile of a student. The same concept can be analogously extended to the use of tools. In certain learning scenarios, students are not given an exact suggestion as to what application to use while working in an activity. A representation of the use of these tools combined with the structural information about the course would place a tool recommendation feature within reach (similar to what some commercial products such as Wakoopa – http://wakoopa.com – are already offering). But link prediction is not the only operation suitable to be applied to SLGs. There are additional operations that can benefit from the proposed representation. Understanding how learning occurs in detail is still an elusive goal, but capturing detailed events and their interaction with various entities opens the door for process mining techniques. Learning is a process, and current assessment techniques are often criticised because they focus on the outcome of a process. A better assessment procedure would focus on how the process of learning occurs, rather than the final outcome. Students currently receive feedback from instructors only at certain points during a learning experience and instructors very seldom are exposed to the process followed by the students. SLGs can provide powerful metrics derived both from structural and event information. Another powerful operation suitable to be derived from SLGs is the many applications trying to improve a learning experience depend on models of knowledge, student behaviour, etc. The information captured and deduced in the graph can be easily translated into categorisation procedures that identify different type of students, resources, topics, difficulty levels, etc. The creation of these models is highly dependent on an additional operation: abstraction. The collection of such a detailed account of all the interactions and relations appearing in a learning environment has the disadvantage that the level of granularity to deduct valuable information is too low. Let us consider, for example, the click stream obtained from a personal computer. Although rich in information, it is difficult to bridge the gap between that information and a higher level relationship such as student U read document D. SLGs offer an efficient representation to apply abstractions. In general abstractions are relations of higher level entities that are derived from partial observations of other entities. In this context, higher level means affecting a large number of entities (student groups, topic in a course, etc.). For example, if over period of time a large number of relationships are encoded including students working in a team project, there could be a procedure that assesses the level of team work and adds the outcome as another relation in the graph. SLGs can accommodate this derived relation thus offering the possibility for graphs to evolve not only in time, but also in derived facts.
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Conclusions and future directions
The last years have shown two clearly identified trends in the web. The increasing number of available resources requires applications to know about the content structure in order to improve search and selection mechanisms and offer users a more personalised experience. Orthogonally to this trend, users reflect in social networking platforms their relations with other users and generate social capital by sharing their personalised experiences within their communities of interest. Both trends have prompted the appearance of multiple representation frameworks and algorithms to support the required functionalities. Semantic web was portrayed as an attempt to attach structure and relations to content. Social networks capture the evolution of relations among users within a community over time. Learning, being as it is a highly social activity that can benefit from personalisation, has been heavily influenced by these two trends. Semantic technology has been applied to observe students and influence the structure of a course, and social networking platforms are being considered as the context in which entire learning experiences can be deployed. But the problem with these platforms is that they have a too wide scope. Learning communities, on the other hand, include a set of users with a common interest and objective. There has been some analysis of the potential of combining knowledge structure with detailed observation of events occurring in a learning environment. But some of the problems of the initial solutions such as the use of ontologies were also present in these proposed paradigms. In this paper an attempt is made to represent learning environments by combining basic information about their structure (without the need of ontologies) and detailed information about the relationship among all entities. SLGs encode this information by representing students, instructors, administrators, tools, resources and even spatial locations as nodes of a graph. The edges of this graph represent relations or interactions of any kind among these entities. This representation can be seen as a tradeoff between the expressive power of its structure, and the possibility of reusing information across various learning environments. SLGs are defined to be built from basic structural information about a learning experience and detailed observations of the events occurring in the environment. Similar representations are already in use by major social networking sites and expanding their functionality. Adopting SLGs in learning environments may open the door for highly effective ILEs based on information already available. A more formal definition of SLGs and its deployment in real-life scenarios are milestones included in the future work. Fortunately, techniques such as link-prediction algorithms, or resource recommendation already have well established procedures to assess their merit. By combining data sets currently available in scenarios where analytic techniques are used with basic information about a course, a set of initial SLGs can be created and analysed to measure their potential.
Acknowledgements Work partially funded by the EEE project, “Plan Nacionalde I+D+I TIN2011-28308C03-01”, and the “Emadrid: Investigaciónydesarrollo detecnologiasparaele-learningenla Comunidad de Madrid” project (S2009/TIC-1650).
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