Concept map of Dron's Control & Constraint in E-learning
Jon Dron's book "Control and Constraint in E-learning: Choosing when to choose" published in 2007 is a work that threads the current e-learning technologies using the concept transactional control. He defined transactional control as the "control exercised or capable of being exercised by an individual in a learning transaction at the point at which a learning trajectory changes direction. (Dron, 2007, p. 333)". It is supposed to be an extension of Michael G. Moore's theory of transactional distance. Dron equates "negotiated control" with dialogue and "teacher control" with structure. In this sense he is similar to Farhad Saba and Rick Shearer (1994) who seemed to have associated "learner control" with dialogue and "instructor control" with structure.
He then used transactional control to evaluate almost all of the current e-learning technologies from electronic publishing to social software. Towards the end Dron advocates social software based on design principles that will provide control specific to the needs of the learner. Dron does not necessarily prescribe total learner control but rather control based on the learner's situation and capabilities. Sometimes the learner would need more control, at other times less but he appears to be in favor of moving towards greater learner control as the learning proceeds in a path. This path is what he called a learning trajectory. Dron defined it as "an individual path that involves decisions about where to go next, what to learn, and how to learn it.(Dron, xiii)"
The pedigree of the "transactional control" is even guaranteed by a foreword by no less than Michael Graham Moore. Moore was even gracious enough to state "...I see the ideas developed in this book as breaking open some of my own primitive ideas...".
Reading Dron's book is like finding order in the chaos that is the study of e-learning. After having read so many journal papers from different disciplines treating individual parts of what Dron had threaded together, I felt like wading through a very murky body of water. Dron's work is like a purifier that made it easier to see what's at the bottom.
I am a bit dissapointed that he used more hypothetical data to explain his model, rather than empirical data. But I guess this is a challenge to future researchers. But when he did provide raw data like in he analyzed Text Chat in Chapter 10 (Dron, p.193) it was exactly like the chat that I had with my fellow online classmates. His analysis hit nail on the head.
But, this does not mean that I'm converted to following Transactional Distance Theory. I still think that the same technologies and concepts could better be explained under network analysis. In fact it would be a challenge to test Dron's conjectures using network analysis. I disagree with the reduction of dialogue to control, which I have modeled simply as communication ties between learner and instructor. I also dislike the imagery of a learning trajectory. I believe that Dron does not imply a single linear path, but when I see the word trajectory I always imagine a cannon ball travelling in a parabola and crashing on the ground. I still prefer the analogy of rhyzomes when dealing with the growth of learning and choices of the learner.
I think I have passed by this book online before but I ignored it because of the cover and the title. The cover is like grandmother's clothes on a bubling teenager. The brown cover does not do the book justice. And the title could be something like "The super duper present and future of e-learning" :-). Okay that was a bad title, but even Terry Anderson found that title confusing (2007).
Why am I reviewing something that was published three years ago? Well I only started studying distance education in 2007, and it took three years for me to even learn the foundation theories of Moore, Holmberg, Peters, etc. So for me it's like trying to catch up with the literature.
In general I think Dron's book is a landmark work in Distance Education that is a great source of hypotheses to test in future researches.
Dron, J. (2007). Control and constraint in e-learning: Choosing when to choose. USA & UK: Idea Group.
Saba, F., & Shearer, R. L. (1994). Verifying key theoretical concepts in a dynamic model of distance education. The American Journal of Distance Education, 8(1), 36-59.
Anderson, T. (2007, April 9). Book Review - Control and Constraint in E-Learning. In Virtual Canuck; Teaching and Learning in a Net-Centric World [Blog]. REtrieved June 29, 2009, from http://terrya.edublogs.org/2007/04/09/book-review-control-and-constraint-in-e-learning/.
In Siemens 2004 seminal paper on connectivism he said, "Know-how and know-what is being supplemented with know-where (the understanding of where to find knowledge needed)."
What I am going to do in this blog is to show how I interpret know-where and know-who using a part of my general connectivist model of distance learning.
Figure 1: Graph of know-where
So what we see from the figure above is that we no longer try to remember the actual content of a document but only it's url or keyword. I personally can never remember Bayes' formula, so whenever I need it I type Bayes' theorem or formula and wikipedia in Google, or in my PC local search engine and even Tomboy notes. Or I locate my book. So what we are actually storing in our brain is an index rather than the content. Perhaps when I use the formula frequently enough I would no longer need to search for it (unlikely :-)). In order to identify the best resource nodes, filters can mediate these ties, like using recommendation systems.
Figure 2: Graph of know-who
Know-who is significant in terms of context. That in today's connected world we can know more people, more experts in the entire globe via computer-mediated-communication. Without the internet, I would have become aware of the Canadians George Siemens and Stephen Downes work probably take years. After their writings had trickled down to my island in the Pacific Ocean. In fact I became aware of connectivism only after my professor Prof. Patricia Arinto pointed it out to me last 2008. That was four years after Siemens seminal paper.
The other thing is in relation to the concept of growing connections as part of growing knowledge. The more people you connect, the more recommendations you can have regarding resources. If you have experts as friends then they are going to provide you with the best filtered sources and your path to knowledge will be much faster than having to scan thousands of periodical abstracts.
Theory driven, data-based, and empirical studies are needed to verify and solidify distance education's conceptual foundation. The project reported here had two main goals: 1) to empirically verify the concepts of transactional distance, structure, and dialogue, and 2) to develop a methodology for achieving the first goal. Drawing on three different fields--distance education, system dynamics, and discourse analysis--the project measured nine key variables in distance education. Results suggest that transactional distance varies by the rate of dialogue and structure, and demonstrate the value of system dynamics modeling for verifying theoretical concepts in distance education. (Saba & Shearer, 1994)
After reading their work as reported in various sources in depth, I must admit that what I am doing for connectivism is similar to what they did for Moore's Transactional Distance Theory (TDT).
Saba & Shearer's Experiment
Using system dynamics Farhad Saba & Rick Shearer attempted to model Moore's transactional distance and fed the model data through an experiment. The experiment involved an individual learner and individual instructor in two separate rooms. They communicate via what we now call videoconferencing. But it was not integrated, instead they had cameras, tv monitors, separate computers with screen sharing, and telephones. The researchers developed a lesson and video recorded the teaching session.
They only analyzed the audio, and used discourse analysis to categorize the discussion. Then they used the frequency of each category as data in the model. So time is also a variable in their experiment. They used STELLA to simulate the interaction in the model.
I got lost with the language of systems dynamics because it is different from graph theory. What I gathered is that the model only used two nodes to represent structure and dialogue. The other TDT elements where kept constant or merged with these two.
They found that there was an inverse relationship between dialogue and structure (as they defined these variables). (Saba & Shearer, 1994)
Difference between Saba & Shearer and my current work
First as I have mentioned is that they modeled TDT, while I am trying to model connectivist distance learning.
Second is that there is a difference in how we modeled transactional distance theory itself. Note that I claim to be able to subsume earlier distance learning theories in my model which is why I showed hypothetically how TDT can be modeled within connectivism.
I think Saba & Shearer only modeled the dynamic attributes of structure and dialogue. And they made all the other elements of TDT constant. Jon Dron (2007, p.22) said that "Saba's merging of learner autonomy and dialogue is slightly at odds with Moore's interpretation...". Furthermore Dron (2007, p.22) said "Moore sees autonomous learning as something quite distinct from that which occurs as a result of dialogue or structure. For Moore, autonomous learners are those who can cherry-pick the sources of learning they need, freed from the impositions of structure or the need for dialogue."
I have modeled learner control (not necessarily learner autonomy) as distinct from dialogue. I consider learner control as vertical a relation between the learner x learning environment x concept. While dialogue is a horizontal relation between actor (including learner) x actor or mediated by the learning environment (e.g. cmc) such that actor-learning_environment-actor. See http://paaralan.blogspot.com/2009/06/interpreting-transactional-distance.html.
Third is that they gathered data from videoconferencing, while my intention is to gather primarily from online learning and then all delivery modes of distance education.
Finally, after 15 years I believe that much has changed not only in technology but also in the tools for network analysis that we can leverage in this approach.
But I am very happy that my classmate pointed this out to me. It's encouraging to find out that my ideas are not that crazy after all. I must give credit to Saba and Shearer for their pioneering work. I hope to learn from their methodology in my own study.
Saba, F. (2003). Distance education theory, methodology, and epistemology: A pragmatic paradigm. In Moore, M.G., & Anderson, W.G. (eds.). Handbook of distance education. New Jersey: Lawrence Erlbaum.
3d visualization blender - 3d visualization if pajek network is exported to to x3d.
Notice that the labels are not imported by Blender. Perhaps you can import the 2d eps to have the labels as paths. My model is not symmetric in 3d because when I was trying to present it in 2d a lot of the lines are on top of each other so I moved nodes so that all the ties can be seen.
Here is the matrix file of the general model, unfortunately it is in one-mode because when I tried to manipulate it in R, embarassing as it is to admit, I totally forgot basic data manipulation in R.
The matrices of the general connectivist model of distance learning are composed of a 3-mode matrix and three 1-mode square matrices.
The 3-mode matrix is made up of the following 2-mode matrices:
actor x learning environment
actor x concept
learning environment x concept
The 1-mode square matrices are:
actor x actor
learning environment x learning environment
concept x concept
Since this is a signed network, these ties could take on negative values as well.
The network model can also factor attribute data with vectors (i.e. n x 1 matrices) that could be vizualized as partitions or sizes of nodes. These are represented in the graph as node properties in general. For example if the actor mode is made up of groups of students engaged in collaborative learning, then the individual students can be partitioned into their groups. There would be a vector of group numbers.
With this model, we can ask a lot of testable questions like, is there a correlation between the change in actor x learning environment ties with the change in the actor x concept ties? What is the probability that y can be known given x sub 1, x sub 2, x sub 3 ... x sub n precedent?
We can compare a learning network centered on an LMS with one centered on a wiki. We can compare the density or average degree of the personal network of one student with another. We could trace paths from start of the course to the end i.e. when goals are achieved, and compare the paths taken by different students to identify the most efficient path.
We can diagnose a misunderstanding by comparing concept networks, or using bayesian network analysis to seek factors that may have contributed to mistakes. We can prescribe better paths, better nodes based on adaptive techniques. User modeling can reside in the concept mode while the mechanisms will reside in the learning environment.
And we can ask all or one of these questions in the model and see the effect of it to the entire network. It is a holistic analysis.
I believe the model can be used to model precedent theories. It will generate similar and new testable hypotheses that relate variables from and across those theories. In other words I think this model synthesizes what has been done in earlier distance education theories.
But this is still a work-in-progress. There is a need to develop the mathematical analysis associated with this kind of network and restate past hypotheses in terms of these computations.
In this paper I will attempt to interpret and visualize Moore's Transactional Distance Theory (TDT) and Holmberg's guided didactic conversation (GDC) through a connectivist perspective. My contention is that connectivism can explicate these precedent distance learning theories (probably with distortion) and may be the universal theory for open and distance learning.
Moore's TDT
Michael G. Moore proposed three concepts that are interrelated in TDT. They are briefly defined below:
Transactional Distance (TD) - a psychological and communication space that separates the learner and the teacher. It the space of potential misunderstanding between the inputs of instructor and those of the learner. Btw. transaction 'connotes the interplay among the environment, the individuals and the patterns of behaviors in a situation' (Boyd and Apps, as cited in Moore, 1997, p.22). For Moore, distance education is a transaction.
Dialogue - is purposeful, constructive and valued by each party. Each party in a dialogue is a respectful and active listener; each is a contributor, and builds on the contributions of the other party or parties....The direction of a dialogue in an educational relationship is towards the improved understanding of the student (Moore, p. 24).
Structure - expresses the rigidity or flexibility of the programme's educational objectives, teaching strategies, and evaluation methods It describes the extent to which an educational programme can accommodate or be responsive to each learner's individual needs (Moore, p. 26).
Learner autonomy - is the extent to which in the teaching/learning relationship, it is the learner rather than the teacher who determines the goals, the learning experiences, and the evaluation decisions of the learning programme (Moore, p. 31).
Structure and autonomy
Let me start with learner autonomy and and structure. These two concepts have been expressed as having an inverse relationship. And that "structure and autonomy can be further represented in relationships that define learner control and instructor control. (Saba, 2003, p.13)". IMHO learner control and instructor control are two states of the concept "locus of control".
How do we visualize this in connectivism? Connectivism in my simplest understanding is the pedagogy of links/connections. (For a better explanation see George Siemens' "Connectivism: A learning theory for the digital age"). So the control is over the links. If expressed in terms of Adaptive Navigation Support (Busilovsky, 2007), it is the ability to generate, remove, hide, sequence, and annotate links. Siemens himself does not negate the possibility of control when he said "Learning is a process that occurs within nebulous environments of shifting core elements – "not entirely" under the control of the individual. (Siemens, 2004) [my emphasis]". And true enough in a networked environment, we will find varying degrees of constraints to our ability to manipulate links. This is particularly true with Learning Management Systems, dubbed as "walled gardens", as oppose to open evironments like those espoused by "loosely coupled teaching" or the principle of "small pieces, loosely joined".
Currently, software developers had implemented the rules of constraints to linking in terms of role permissions. For instance, in Moodle, the student has no permission to create links in the course page. Only users with the role editing teachers, course editors, and administrators have those permissions. Of course the permissions can be changed by those who have permissions to grant permissions (usually the administrator).
To model the change in the locus of control over time in a system, I have graphed a closed learning network with three actors namely: a student, teacher and an adaptive system or machine. To simplify what I my illustration of a shifting control over the network in a turn-based manner, I have made the links static. But you can imagine that when an actor has grabbed control over the network of links then he/she/it can manipulate the nodes and links to her/his/it needs and wants. Turn based concept usually involve locking the resource being edited at the time by an actor, such that the other actors may not edit it at the same time.
The graphs below are multi-relational, 3-mode, signed graphs. The relations are color coded. Each relation (or set of links in one color) is owned by an actor. It can be interpreted as his/her personal learning space. But in this case, it is interpreted as power relations between actors, in relation to their learning environment, and the concept level. I have expressed the control as thicker lines.
Figure 1: Equalibrium (Time 0) Figure 2: Time 1, Teacher has control Figure 3: Time 2, Student has control Figure 4: Time 3, Adaptive System has control
Figure 5: Animated video of turn-based passing of locus of control
The passing of control does not have to be turn-based at all. And it does not have to be control over the entire system. In an open environment, certain parts of the network are in the control of one person at the same time as other parts of the network are controlled by others. I will try to model this in another blog later on, as it could get very complicated. But you can think of let's say a student keeping a blog outside the LMS. It is connected to the LMS only via RSS but the teacher has no control over the blog.
When viewed this way, the term structure by Moore is not equivalent to structure in connectivism. Moore's structure is only a subset of many patterns of control over the network at different time period. It is equivalent to a structure that is NOT controlled by the student.
Control is usually described as control of sequencing the learning resources or links to the resources. This is expressed below as arcs in the learning environment level. Figure 6a: No sequencing Figure 6b: Sequencing by student and teacher
Fig.6b above shows that there is a contradiction between how the student sequence the resources and how the teacher sequence them. The teacher starts from resource 1 and ends with resource 3; but the student start from resource 3 and work backwards.
Control can also be expressed as control over the concepts themselves, especially when there is assessment of the personal conceptual network of the student.
Dialogue
Dialogue can be modelled as links between actors, or mediated by the learning environment. Take note that Moore's dialogue is always positive, which may not be the case when modelled here. Figure 7: Dialogue between student and teacher Distance
In my model of distance, I hooked on the concept of misunderstanding. IMHO misunderstanding occurs at the concept level. It is expressed here as the difference between the personal conceptual subnetwork of the learner and that of the teacher. Here are three possible interpretations of distance: Figure 8: Distance 1
In Fig.8 the student has not linked the concepts, while the teacher has linked them. We may say that the student does not see any connection, may be confused, or is a beginner. But these are value statements. The graph does not state that the teacher's version is correct, and the student is wrong. It simply states that there is a difference between the coneptual network of student and teacher. Figure 9. Distance 2
In Fig. 9 the student has linked concept 1 and concept 2 but not concept 3. Can we say then that this student has less distance with the teacher than that in Fig.8? Figure 10. Distance 3
In Fig.10, the student has linked the concepts like the teacher, but negatively. We can interpret it as that the student thinks the concepts are contradictory, or conflicting. Definitely, the teacher and student are in disagreement.
In addition, if we go back to Fig. 6b you'll notice that there is no difference between the conceptual network of the student and the teacher, despite the fact that they sequenced the resources contradictorilly. Is this possible in real life? Only empirical data will show.
So in summary there are many patterns of interpreting transactional distance in connectivism. What excites me about this is that, it is a formal presentation wherein we can apply mathematical techniques. Although I still don't know what computational analysis is appropriate.
Using Pajek I was able to extract the individual relations from the network above. And here they are for clarity. Figure 11: Student Control Figure 12: Teacher Control Figure 13: Adaptive System Control
Holmberg's Guided Didactic Conversation
Let me now turn to Borje Holmberg's theory. Unfortunately I've lost my reference while I'm typing this, so I will try to reconstruct what I remember about it. I belive it's a conversation of the student with himself as a reflection of content. It is therefore a relation between the student and the resources of the learning environment. But if we push back the time slice far enough, each resource will have an actor developer, author, etc. node associated with it. That is why I usually call these ghost nodes. But since the teacher in distance learning is also not physically present before the learner, is the teacher a ghost in the machine as well?
Anyway, I've modelled it below. Figure 14: Guided Didactic Conversation in a Network
I interpret it as a process of increasing or decreasing the link property e.g. significance, usefulness, relevance, etc. between actor and the learning environment's resources. This is visualized as varying line weights and varying distances of nodes relative to the student node.
References
Brusilovsky, P. (2007). Adaptive navigation support. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web, methods and strategies of web personalization. Springer, pp.263-290.
Moore, M. "Theory of transactional distance." In Keegan, D., ed. "Theoretical Principles of Distance Education (1997), Routledge, pp. 22-38.
Saba, F. (2003). Distance education theory, methodology, and epistemology: A pragmatic paradigm. In Moore, M.G., & Anderson, W.G. (eds.) Handbook of distance education. New Jersey: Lawrence Erlbaum.
Seen from 2d on top doesn't show the different sets of nodes fully, but it does show it as partitions. But if spinned at 90 degrees we can see that there are at least three sets. The human actors e.g. teachers and students; the physical learning environment e.g. a course page and linked documents; and the concepts which could be ideas, processes, tasks, goals, beliefs etc.
Fig. 2: network at 90 degrees spin
Note also above that I consider the concept level as the level of abstraction that is used for student modelling, at least with an knowledge overlay method.
My problem with the method of analysis of this network is that a 2-mode network graphing already demands that "In a two-mode network, vertices are divided into two sets and vertices can only be related to vertices in the other set. (De Nooy, Mrvar, & Batagelj, 2005, p. 103)" and I have not yet found the methodology for k-node, or 3-node networks.
Ref:
De Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory network analysis with Pajek. Cambridge: Cambridge University.
My 3 week connectivist learning in a University of the Philippines Open University course on Research in Distance Education.
This is a research project I am trying to explore in this course. I conceive the ties between nodes in terms of the following. It's a scale from 1 to 5. The nodes can be actors, beliefs, digital resources and real world resources.
Here are example of my interpretation of ties between actors and resources.
Understand - actor reads resource and understands (there is no need to demonstrate the understanding since this is self-reported. This is the point when if asked "What is the resource about?" you can explain it."
Apply - actor uses the information in explaining existing phenomena or data without changing it. If the resource involves processes then he/she can follow instructions.
Analyze - breaks down the elements of the resource, including unwritten assumptions.
Evaluate - judges the resource as being relevant or irrelevant. Find errors in argument. Defends the resource position in a debate or argumentation.
Create - the actor is moved to do something after reading the resource. Either against or for the ideas of the resource. e.g. blog in favor or against the resource. Do research.
The following is a descriptive-qualitative interpretation. Quantitative network analysis can be performed later on. I have not gotten the hang of that yet. Fig. 1: Graph of Time 1 of hypothetical learning data Fig. 1: Matrix of Time 1 data
Matrix one shows a student becoming aware of 4 resources (could be pdf readings, videos, etc.). But the student is only collecting the links and has not developed any connections among them, or to his own concepts/tasks.
Fig. 3: Graph of Time 2 data Fig.4: Matrix of Time 2 data
The student has clustered the resources into two concepts. This clustering is similar to when we tag videos, or blogs. The tags serves as the conceptual category and becomes a hub for resources. Student 1 also found links among his/her resources. Notice here that the student is an independent learner.
Fig. 5: Graph of Time 3 data Fig. 6: Matrix of Time 3 data
This is more complex. Student 1, with his concepts and resources, links to a course via forums and email. He/she has a teacher (Faculty-in-Charge or FIC) and another classmate (Student 2). Here student 1 becomes aware of the FIC's concept/task being taught. Student on the other hand is attracted to Student 1's concept 2 as well as the FIC. But the links here are still shallow at line weight of one.
Fig. 7: Graph of Time 4 data Fig. 8: Matrix of Time 4 data
There is no difference between this matrix/graph and the preceding except line weights. The thicker line weights may be interpreted as more relevant resources, concepts etc. for particular students. Here Student 1 prefers his first concept and the associated resources (1 & 2), while Student 2 prefers the FIC's concept and therefore resources (5 & 6). We might interpret Student 1 as a more autonomous learner than student 2 at this point,but there could be other interpretations.
What is the significance of all this data to Adaptive Learning
The point here is that if we can quantify the lessons from all these data and find a pattern by comparing the partition of Student's or growth of their learning over time we will be able to identify resources that will optimize the learning in this domain. We can compare student's connectivist' learning and create user models or on task models from that. We can then feed this finding to an Adaptive Educational System (AES), such that when another student in the next semester goes through this course, the system can recommend the resources based on the type of student he/she is (e.g. student type 1 or student type 2). Or if the course is constructivist, then the concept nodes will be replaced by tasks or activities.
It's as if the AES recommendation system would be able to say to the student "Other students found this resource (provides link) useful to the concept/problem/tasks you are exploring. You may also want to check these other resources out." The student can then preview the recommendation and either reject or affirm it's relevance. This new data would then be fed into the system, and so on. This system therefore does not preclude the student or the teacher from recommending other tasks/problems/resources to their peers or students.
Here is the entire series animated:
Links:
Pajek - a program, for Windows, for analysis and visualization of large networks. It is is free for noncommercial use.
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