I would work on a model that could predict not only spelling and grammar mistakes but that could help investigating why these mistakes were made. And I would ban the words “mistake” & “error” from this model because defining something as such has drastic consequences: the assumption that there is only (a predefined) right or wrong. (Whereas I have to admit that I am still looking for an adequate substitute). Questioning why is made so much harder – because obviously simply choosing the right answer by a right click in the spelling check software saves a lot of time.
While this might appear to be an unsolvable quest at first sight, I think there are numerous patterns that could help to develop such a model. This text is written in English. But I am not a native speaker. I do speak 3.5 languages. Starting a new one is always a special challenge. Because the more languages you learn, the more context exists to frame the new language. This can be good. You know, which words you have to learn first for daily conversations, which grammar structures are particularly useful. You know where to start.
But one thing always stays the same: I am trying to find out WHY I make certain mistakes. This goes beyond simply knowing THAT I made a mistake. It is to find out WHERE I got the structure or word from I am not applying adequatly. It is to understand that the mistake could possibly be correct in another language, make the connection to the current language and store this connection.
(This is how it works for me. And I think that others can benefit from this as well. I am not a professional linguist so this is not grounded on any well-established theory or such. I guess there might be several research undermining or supporting this idea.)
Bist du das? ≠= Are you it?
Literal interpretation is one example. While the English native would understand at least what “Are you it?” is supposed to mean (“Is it you?”) – does this mean that the translation is wrong? Yes – says the English teacher. No – I say.  I speak both languages and thus can make sense out of it and know what the other person wanted to ask. Isn’t that weird? At first it seemed so plain that the question “Are you it?” is wrong – but why would some people then understand it? Because of the similar background/ context (of language) they have. Because they understand WHY someone translated the question this way.
We have so much data sources to choose from for translations. Why can’t we make the spelling check process more individual? One could implement settings to choose native language and other languages learned so far. When a mistake is detected, a model could be applied to detect if this is a simple typo or a systematic error that can / can not be connected to another language. Imagine the potential of evaluating writing patterns (e.g. as already available for messaging in Android systems: the system is guessing your next word): You could get a summary of frequently occurring mistakes and working on these in future.
Until my breakthrough with my “why-you-made-this-mistake-model”
Could I ask you for a favor? If you know someone in your social environment who is learning a new language and he/she is asking for a word: Please don’t just translate the word into a language that is easier for him/her! Try to explain the word in the language he/she is learning and give as much context as possible. It helps a lot. I know, it’s not always easier. Because the faster way is a simple translation (like the right-click in your spell-check). But on the long-run, putting language in context is worth the additional expenditure of time.
Social Network Analysis in the area of conflict between being analytic &Â considering educational research
Discussing assets and drawbacks of learning analytics (LA) and social network analysis (SNA) in particular leads to discussing the objectivity of data collection. Applying these methods not only enables live-data tracking and analysis (as opposed to self-reported pre-/post-course surveys) but the objective analysis of learner’s traces in the learning environment (though this does not account for a complete picture of the learning which takes place). However, the potential stays untapped if underlying pedagogical and epistemological assumptions are not taken into account.
Gašević, D., Dawson, S., Siemens, G. (2015) point this out as they claim LA to connect existing research on learning, teaching, educational research and practice. They bemoan missing studies which evaluate the concept of the established lead indicators of LA. Three major themes in their paper are the detailed description of how tools are used, looking beyond frequency of activity and time spent on task by analyzing individual learning strategies and products  and the connection of internal and external conditions for data. Within this context the base for effective LA visualizations lies in the consideration of instructional, learning and sense-making benefits – foundations in distributed cognition and self-regulated learning being a potential direction of future research.
“The Epistemology–Assessment–Pedagogy triad”
Knight, S. et al. (2014) identify LA as implicitly or explicitly promoting particular assessment regimes in the epistemology, assessment and pedagogy triad. A wonderful, more detailed reflection on this triad in connection to learning analytics can be found here (authored by Classroomaid). Suthers, D. D. & Verbert, K. (2013) define LA as the “middle space” between learning and analytics. In their paper they elaborate three main themes for future research in the field of LA: “the middle space” (focus on the intersection between learning and analytics, avoiding the emphasis of one of these themes), “productive multivocality” (facing the challenge of unifying a multifaceted research field by focusing on analyzing a common data ground) and “the old and the new” (enhancing learning as a century-old idea that is continuously accompanied by new tools) (pp1). Given the rich online learning landscape, clustering learning environments can be the first step of detecting characteristics, underlying epistemology-assessment-pedagogy beliefs and thus identifying the appropriate measures of learning analytics.
“The Epistemology–Assessment–Pedagogy triad”, adapted from Knight, S. et al. (2014), p4
For example Rodriguez (2012) classified MOOCs as either c-MOOCs (following the connectivist tradition) or x-MOOCs (“AI-Standford like courses”, following the cognitive-behaviorist tradition). It is important to note here, that the term “x-MOOCs” was not coined by Rodriguez, but Liyanagunawardena, T., et al. (2013) establish ties to Daniel (2012), where they detected similar definitions and thus combined both papers. For his classification Rogriguez used Anderson & Dron’s (2011) paper on “Three Generations of Distance Education Pedagogy” where they coin three DE pedagogy concepts. Bringing the triad back into focus: it’s the pedagogy concept that supports his classification – differentiating between connectivistic and cognitive-behavioristic pedagogy.
The implication of this classification is a different view on teaching, social and cognitive presence in the online learning environment. This view needs to be considered when analyzing the underlying epistemological concept and the assessment formats. Besides common features, this relates especially to the role of course instructors, the definition of openness (access vs. openness to personalized learning), connectedness and guidance. Knowledge is either generative (c-MOOC) or declarative (x-MOOC). By saying so, without a coherent triad the best assessment strategy does not tackle the real learning happening. Furthermore, the triad can be used to continuously challenge the assumptions of each corner.
Active Learning – an example for effective college classroom practice
One example of this triangular interplay is active learning. Grunspan et al. (2014) mention the effectiveness of active learning in college classrooms and as a result they explore this practice with the support of Social Network Analysis. However, the triad does not implicitly determine direction and interdependencies. Critical reflection of all intersections is necessary. This exemplifies the importance of meta studies, e.g. “What makes great teaching? Review of the underpinning research” by Robert Coe, Cesare Aloisi, Steve Higgins and Lee Elliot Major (2014). A call for a constant challenge of learning assumptions, the participation in the on-going research process and the relation and integration of own research efforts go hand in hand with the importance of comparing different underlying concepts and critically questioning if researchers are particularly considering the same concepts. As one example of this meta study, active learning and its outcomes for the learning progress seem to contradict Grundspan et. al. By relating to a learning pyramid, Coe et al. argue that memory and remembering is not evidently based on being better when participating actively or passively (“Ensure learners are always active, rather than listening passively, if you want them to remember (p24)). Simply the different level of complexity of both concepts (active learning and active/passive listening in relation to memorizing) discloses that we are not exposed to the same ideas here. However, these approaches seem to be connected and point in opposing directions of sound research evidence. When validating and examining existing research, it becomes more and more important to nail the underlying assumptions and research questions to create reliable conclusions for further research.
Six different case studies: Learning design can influence learner’s decision
The course presents six different case studies which emphasize the claim that learning design can influence learner’s decision. By doing so, the above triad is underpinned again – not only focusing on practical applications but also giving a direction for future research approaches that critically question underlying concepts of epistemology, pedagogy and assessment.
1. Instructor-centered network: Bringing together SNA and learning design [Lockyer, L., Heathcote, E., & Dawson, S. (2013)]
Lockyer et al. explore how the framework for the interpretation of LA might lie in the learning design. By using case-based learning they examine a concept of checkpoint and process analytics to analyse learning design embedded in a context, in real-time and behavior-based (a more narrowed-down application of LA). Hence, learning design and analytic design are connected to support learning and teaching decisions. They also propose different directions for future research including “engaging teachers and students in understanding and using visual patterns of interaction as a means to encourage learning activity; scaling up to larger numbers of classes, providing a base for comparing statistically the observed to expected analytics of behaviors and interactions; and using results to provide meaningful feedback to teachers on how their learning design is meeting their pedagogical goal and to assist them in decisions around design and pedagogical change in real time.” (p1455)
2. Sense of community [Dawson, S. (2008)]
The research question in this study is “Is the composition of social networks evolving from a unit discussion forum related to the sense of community experienced among the student cohort?” (p226). In general, this deals with the question of belonging vs. isolation, or better: with the extend to which learners benefit educationally from belonging and how this can operate as a predictor for students success (as social integration is strongest predictor for retention and completing university degree). The underlying educational concept is community-centered teaching practise, based on social-constructivistic ideas of Dewey and Vygotsky. The novelty with this study is that self-reported surveys are not the only data source anymore. It is a mixed method approach used here which focuses on quantitative (Classroom Community Scale, SNA centrality measures) and qualitative measures (discussion forum content, student interviews). As a result, Dawson found a association between the network position and the sense of community, in detail a positive association with closeness and degree centrality; a negative with betweenness centrality (dilemma of brokerage). In addition, pre-existing external social networks influence the type of support/information required. Concerning future research Dawson points towards the investigation of the relation between social networks and other measures having an influence on the learning environment such as pedagogy, practitioner personality and cohort demographic profiles.
3. Network brokers associated with achievement and creativity [Dawson, S., Tan, J. P. L., & McWilliam, E. (2011)]
Dawson, S. et al. discuss the correlation of cognitive playfulness to the network position where degree and betweenness centrality are oppositional to closeness (as they are positive indicators of a learner’s creative capacity) . By answering their research questions “What is the relationship between a student’s social network position and perceived creative capacity? To what extent do discussion forum mediated social networks allow for the identification and development of student creativity?” they claim that SNA can provide insight in the creativity of students as well as a tool for instructors to monitor the learner’s creative capacity level. The individual’s self-reported creativity score thus corresponds with the overall social network position. Creativity is perceived as highly valued graduate asset.
4. SNA for understanding and predicting academic performance [Gašević, D., Zouaq, A., Jenzen, R. (2013)]
Gašević, D. et al. studied cross-class networks and the importance of weak ties by considering the relationship between academic performance and socal ties. The base for this study is social capital and network learning research. Two hypothesis where investigated: (1) “students’ social capital accumulated through their course progression is positively associated with their academic performance”; and (2) “students with more social capital have a significantly higher academic performance.” Based on the ideas of Vygotsky one practical implication is the conception of new social ties in each course during degree programs.
5. SNA and social presence (What is the association between network position and social presence?) [Kovanović, V., Joksimović, S., Gašević, D., Hatala, M.]
This study focuses on the Community of Inquiry model, specifically on the social presence as one contributor to educational experience. Social presence consists of three parts, namely affectivity and expression, interactivity and open communication and cohesiveness. By analyzing the underlying social processes that contribute to the development of social capital, Kovanović, V. et al. give an insight in how affective, cohesive and interactive facets of social presence significantly predict the network centrality measures commonly used for measurement of social capital. Social constructivist pedagogies and the shift towards collaborative learning can be seen as underlying educational concepts. The research question “What is the relationship between the students’ social capital, as captured by social network centrality measures, and students’ social presence, as defined by the three categories in the Community of Inquiry model?” (p3) leads to the results that interactive social present is “most strongly associated with all of the network centrality measures, indicating a significant relation with the development of the students’ social capital.” In conclusion, in-degree and out-degree centrality measures were predicted by all categories of  social presence whereas betweenness centrality was predicted by interactive and affective categories.
6. SNA and understanding of MOOCS [Skrypnyk, O., Joksimović, S. Kovanović, V., Gasevic, D., Dawson, S. (2014)]
Skrypnyk, O. et al. explore the learning environment of a cMOOC to identify and understand important key actors. Although in this study the facilitators continued to occupy a central role, other actors emerged and complemented this picture. This was based on the two research questions “What is the influence of original course facilitators, course participants (ie., learners) technological affordances on information flows in different stages of a cMOOC?” and “What are the major factors that influence the formation of communities of learners within the social network developed around a cMOOC?”. As a result, types of authorities can be classified as “hyperactive aggregators” and “less visible yet influential authorities”. For the former, there might be an existing connection to natural personality traits. Another outcome is the importance of hashtags for information flow and community construction within a more learner-centered environment supported by software.
Application in Gephi and Tableau
By combining Gephi and Tableau I created a dashboard, where it is possible to see different ways of visualizing the same data source. Please click the image to enlarge it and see descriptions for the single sheets on the dashboard. As my first attempt, this dashboard shall illustrate how conclusions could be drawn from one single glance.
For example, the network analysis with Gephi detects the network structure and reveals node 3 as a central key actor when it comes to the measure of degree (top right). 9, 10 and 11 follow with some distance. The same pattern can be detected in the top left visualization, where size represents degree as well, but the color reveals betweenness. We can conclude, that degree and betweenness are correlated, as a decreasing quantity of connected nodes goes hand in hand with a decreasing betweenness (not surprisingly, but due to the image more visual). The Degree sheet down left specifies in- and out-degree values – again we see the network key players 3, 9, 10, 11 but here we can specify their communication patterns. Whereas 3 has the highest out-degree but the smallest in-degree, it’s actor 11 that has the highest in-degree and the smallest out-degree. Dependent on the underlying question, we could draw better conclusions at a glance from this data.
Anderson, T. and Dron, J. (2011). Three Generations of Distance Education Pedagogy,International Review of Research in Open and Distance Learning, Volume 12, Number 3. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/890/1663
Classroomaid (2014-11-14). Our Learning Analytics are Our Pedagogy, Are They? (#xAPI, #dalmooc), retrieved on 2014/10/23 from http://classroom-aid.com/2014/11/14/our-learning-analytics-are-our-pedagogy-are-they-xapi-dalmooc/
Daniel, J 2012. Making Sense of MOOCs: Musings in a Maze of Myth, Paradox and Possibility.Journal of Interactive Media in Education 2012(3):18, DOI: http://dx.doi.org/10.5334/2012-18
Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224–238 (full text).
Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technology, 27(6), 924-942 (full text).
Edu_k (2014/11/14). Social capital in SNA for LA – too much focus on individuals at a cost of the group, retrieved on 2014/11/24 from http://nauczanki.wordpress.com/2014/11/14/social-capital-in-sna-for-la-too-much-focus-on-individuals-at-a-cost-of-the-group/
Gašević, D., Zouaq, A., Jenzen, R. (2013). Choose your Classmates, your GPA is at Stake!’ The Association of Cross-Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459-1478. doi: 10.1177/0002764213479362 (full text).
Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research. CBE-Life Sciences Education, 13(2), 167–178. doi:10.1187/cbe.13-08-0162 (full text)
Knight, Simon; Buckingham Shum, Simon and Littleton, Karen (2014). Epistemology, assessment, pedagogy:
where learning meets analytics in the middle space. Journal of Learning Analytics (In press).
Kovanović, V., Joksimović, S., Gašević, D., Hatala, M., “What is the source of social capital? The association between social network position and social presence in communities of inquiry,” In Proceedings of 7thInternational Conference on Educational Data Mining – Workshops, London, UK, 2014 (full text).
Liyanagunawardena, T., Adams, A., & Williams, S. (2013). MOOCs: A systematic study of the published literature 2008-2012. The International Review Of Research In Open And Distance Learning, 14(3), 202-227. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1455/2531
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459, doi:10.1177/0002764213479367 (full text).
Rodriguez, C. O. (2012). MOOCs and the AI-Stanford like courses: Two successful and distinct course formats for massive open online courses. European Journal of Open, Distance and E-Learning. Retrieved from http://www.eurodl.org/?p=Special&sp=init2&article=516
Suthers, D. D., & Verbert, K. (2013). Learning analytics as a “middle space.” In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 1–4). New York, NY, USA: ACM. doi:10.1145/2460296.2460298
Skrypnyk, O., Joksimović, S. Kovanović, V., Gasevic, D., Dawson, S. (2014). Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. British Journal of Educational Technology(submitted) (full text).
This post is one of the longest I have written so far (it took my about a week to finish it), and I still have some open questions:Â
[…] what determines the centrality of single nodes or clusters [when applying layouts in Gephi]?Â
[…] what [does] the modularity class tell me about the community?
Does this exemplify the importance of additional measures besides “degree”? As the number of connections does not necessarily correspond to the ability to serve as a bridge?
Isn’t it logical when being part of a smaller sub cluster that closeness centrality and eccentricity decline as smaller groups are per se connected to fewer nodes in the network?Â
These questions can be found in the text marked in bold where they are also embedded in the context to offer more information. I would be very happy, if people interested in Social Network Analysis (and Gephi) could help me finding the answers. Thank you in advance 🙂
Social Network Analysis (SNA) – that has a familiar ring!
When hearing about SNA in week 3, I remembered reading this term in some of the last weeks’ resources. Baker, R., & Siemens, G. (2014) frame SNA as one of four approaches of structure discovery of data in EDM/LA which can be seen as opposite to prediction (there is no priori idea of a predicted variable). SNA reveals the structure of interaction by analyzing the relationship between individual actors. Shum & Ferguson introduce SNA as a “possibility” offered by Social Learning Analytics (SLA). This possibility is social itself – as for example discourse analysis – but opposed to social learning disposition analytics or social learning content analytics, which need to be “socialized” first. So again, this sounds very social, and I am wondering if Social Learning Analytics and Social Network Analysis can combine three of my areas of interest: (Social) Psychology, Learning and Analytics.
SNA – What are we talking about?
Social Psychology – a friend I believed to be lost – is striking back as powerful and drawing interest as usual. Burt, Kilduff & Tasselli (2013) mention two facts established by Social Psychology that network models of advantage (“as a function of breadth, timing and arbitrage) build upon. These are
people form groups based on where they meet
within a group communication is more influencing and frequent than between different groups (as similar views develop).
As potential sources they mention Festinger et al. 1950 – which developed the idea of group cohesiveness and on page 151 of their book it says
The gist of these conclusions may be summarized as follows: In a community of people who are homogeneous with respect to many of the factors arising from the arrangement of houses are major determinants of what friendship will develop and what social groupings will be formed. These social groupings create channels of communication for the flow of information and opinions. Standards for attitudes and behavior relevant to the functioning of the social group develop, with resulting uniformity among the members of the group. Other people deviate because they were never in communication with the group.
This is a concept we can observe in our daily life. Imagine a university course starting and some students joining a little later than the rest. A loose group has already formed, the information flow is established. A new member joins this network and wants to put herself on equal footings with the others. Group cohesiveness represents “the property of a group that effectively binds people, as group members, to one another and to the group as a whole, giving the group a sense of solidarity and oneness.” (Hogg, M. A. & Vaughan, G. M. (2014), p288). In this respect one has to bear in mind that a lot of consequences result from cohesiveness, where positive and negative lie close to each other. In- and out-groups form, which can be positive for the in-group members. On the other hand, out-group members are excluded, the base for the emergence of prejudice. Many more social phenomena could be named here – something SNA may not overlook.
Haythornthwaite (1996) combines the social and analytics perspectives on Social Network Analysis in an interesting way. She mentions that compared to other analysis techniques SNA focuses on relationships and their patterns and contents. Thus it “strives to derive social structure empirically, based on observed relationships between actors, rather than on a priori classifications” (p325). The world is hence explained by networks, not by groups. Relationships and ties are described in relation to content, direction and strength (I will focus on the two latter ones).
Direction is asymmetrical when the information flow is one-way only and direct/undirect classifications represent if the direction of flow is either not measured/not considered relevant. In terms of strength (intensity of a relationship, in addition to the mere existence) either the number of ties and/or their strength can be examined. Haythornthwaite is introducing five network principles:
cohesion (grouping nodes regarding strong common relationships by e.g. density and centralization; clusters and cliques)
density (degree to which members are connected to all other members)
centralization (extend to which a set of actors are organized around a central point)
clusters (subgroup of highly interconnected actors)
cliques (fully connected clusters)
structural equivalence (grouping nodes regarding their similarity)
prominence (the node in charge)
centrality (differs from centralization as it measures the node’s connections in the network rather than measuring the configuration of the  network)
global centrality or closeness (shortest path between an actor and every other actor in the network)
range (a node’s network extent) and
brokerage (bridging connections to other networks)
betweenness (extent to which an actor sits between others in the network, playing a role as an intermediary)
So if we see SNA as the source of tools for the analysis of relational data (Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014)), we can detect two classes of hypothesis: why are relations formed and what are the outcomes of these relations? They also argue, that these questions are important, as the learner’s position seems to be correlated with her performance. So in order to understand networks, we need to understand the determinants, structure and consequences of relationship between actors. That needs to be considered in situations where social support / connections are to influence the outcomes of interest.
With this in mind, the main methods for SNA are modularity, density and centrality.
Modularity describes a way of quantifying the concept of community structure. In brief, it is calculated by subtracting the number of ties falling within groups subtracted from the expected ties in a similar network with nodes placed at random. [Newman, M. E. J. (2006)] This concept incorporates the idea that simply taking the number of ties between two groups one would expect at random is not meaningful until there is no comparison between expected numbers and actual present numbers.
Density in the words of Hanneman & Riddle (2005) is “the proportion of all possible ties that are actually present.” It is calculated by forming the sum of ties divided by the number of possible ties. In a fully connected network or subgroup of a network (a clique) would have the density of 1.
Network types can be described as unipartie (one type of actors) vs. bipartie (actors linked with the group to which they belong); undirected vs. directed (please see the Facebook network example below) as well as binary (simple existence) vs. valued ties (additional quantitative data). When talking about actor-level variables there are several proposed measurements of centrality existing: degree centrality (total number of connections a node has; in directional relation networks including in- and out-degree), betweenness centrality (actors serving as bridges in the shortest paths between two actors), closeness (how close one actor is to other actors in average) and eigenvector centrality (being connected to other well-connected nodes) (based on Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014)).
SNA in action – My Facebook network analysis in Gephi
I was surprised how easy it actually was to gather information around my Facebook network. And in addition, it felt weird. After receiving the data by running the Facebook app Netvizz (which creates a file that can be used by Gephi to analyse the network) I decided not to mention individual personal data to avoid drawing detailed conclusions from the below graphs. They are used to visualize the main methods introduced above and to briefly discuss some striking results of the analysis.
My Facebook network visualized in Gephi – First attempt
Gephi can be used for both – visualizing a network in the format of sociographs and identifying datasets of individual actors (nodes) and clusters in this dataset. The first step after running Netvizz is to integrate the dataset as a directed or an undirected network. As I am dealing with Facebook, the idea of a connection between two actors (a tie between two nodes) is that both agree to become Facebook friends. Thus, the direction is not relevant for the analysis. Hirst, T. (2010, April 16) describes this in his blog, comparing Facebook to Twitter, where direction would matter. As a result, I chose an undirected network for my analysis. From the import report we can see, that there are 320 actors (friends) in my network, connected by 3182 ties (connections).
Gephi import report for my Facebook network analysis
After pressing the OK button, the network looks similar to the first graph of this section. It is an “imbroglio” of nodes and ties, not really interpretable. So it would be useful to apply some methods introduced above, starting with density.
The density has been calculated with 0,062. Strictly speaking, my Facebook network does not take advantage of its potential as it is not using all the possible connections. Whereas this might correspond to the basic interpretation of density, this is only applicable in a limited way here: there are many subgroups in my network that might have a higher density. In addition, one has to interpret the word “potential” here: yes, there are potential connections that could be made, but does that mean, they would change the quality, or the information flow of the network?
Applying nodes ranking degree (color) in GephiApplying nodes ranking degree (size) in Gephi
For a more interpretable visualization, I applied measurements of centrality. The first one is degree centrality, offering the total number of connection a node has (as we are dealing with a indirectional relation network, there is no in- and out-degree). In my network, there is one actor with the highest degree centrality followed by three friends with a quite similar degree centrality – all of them are in my eyes well-connected in Facebook terms. But one has to be careful about making any qualitative judgements: degree centrality is offering an overview of connections quantity, not quality.
There are so many layouts in Gephi to apply for a better visualization but I have no idea of how they work in particular. In my case, I was applying Fruchterman Reingold (“a classical layout algorithm, since 1984; rated with 2/5 stars in quality, 3/5 in speed”). So the result looks like this:
Applying the Fruchterman Reingold layout in Gephi
From my understanding, the layout visualizes sub clusters of the network and arranges them dependend on how close they are to each other in a given area of a circle. [1] However, I am not quite sure what determines the centrality (here ment to be literally as being arranged more centrally in the area of the circle) of single nodes or clusters. What is striking in my Facebook network is that the formerly defined high-degree actors all belong to one cluster (the upper right one), and in addition we can find two more clusters. In general, it seems applicable to define these clusters as the “home-cluster”, the “work-cluster” and the “university cluster”.
Let’s go for the modularity now to identify more relation patterns in this network. The results are as follows: there are 23 communities. In the size distribution chart, the number of nodes (size) is related to the modularity class. [2] In this context, I am not sure, what the modularity class tells me about the community. The biggest community has about 90 nodes and a modularity class of 19. Whereas there are some small communities with only 1 node but a higher modularity classes.
Gephi modularity report for my Facebook network
To get rid of too small communities, my next idea was to filter out groups that are smaller than 3 nodes. (This can be done by setting the degree range filter in the topology folder and changing it to the range of 3-70). Running the density and modularity statistics again resulted in a density of 0,08 now (former:Â 0,062), 9 communities instead of 23 and a slighty lower modularity of 0,686.
Modularity report after filtering the degree range 3-70
Some additional measures and information: By filtering the nodes, the overall number was reduced to 281 actors and 3142 connections. The average degree of an actors is 22,363 and the network diameter 6 (meaning that the greatest path between two actors is covered by 6 nodes).
My Facebook network with the node partition modularity class and the nodes ranking (size) of betweenness centrality
The partition function in Gephi is useful to visualize the 9 identified communities (I had to re-apply the layout, so now the position of the communities does not correspond completely to the former graphs, it is turned by 45 degree clockwise). In addition to this partition by modularity class I applied the betweenness centrality measure (actors serving as bridges in the shortest path between two actors). I am focusing on two actors here (the two biggest nodes, one light green, one light blue). Whereas the light blue one corresponds to one of the actors identified by the degree measure, the light green actors is a “new” one, not identified by the degree measure before. [3] Does this exemplify the importance of additional measures besides degree? As the number of connections does not necessarily correspond to the ability to serve as a bridge? In more quantified terms, betweenness centrality measures how often a node appears on shortest paths between nodes in the network. So actors identified by this measure could be valuable for transfering information from one group to the other. And believe it or not, the actor indentified by Gephi is a friend of mine that I always ask for “the gossip” and he/she knows almost everything about different groups – without being connected to most of the people in my network.
My Facebook network with the node partition modularity class and the nodes ranking (size) of closeness centralityMy Facebook network with the node partition modularity class and the nodes ranking (size) of eccentricity
Two additional measures I applied can be seen in the graphs above: closeness centrality (as how close one actor is to other actors in average or the average distance from a given starting node to all other nodes in the network) and eccentricity (the distance from a given starting node to the farthest node from it in the network). The most striking fact is that there is no actor of the network explicitly standing out from the rest. However, in smaller communities the centrality seems to be lower with both measures. Should this be surprising? [4] Isn’t it logical when being part of a smaller sub cluster that closeness centrality and eccentricity decline as smaller groups are per se connected to fewer nodes in the network?Â
Burt, R. S., Kilduff, M., & Tasselli, S. (2013). Social network analysis: foundations and frontiers on advantage. Annual review of psychology, 64, 527-547. doi: 10.1146/annurev-psych-113011-143828 (full text)
Festinger, L., Schachter, S., Back KW (1950). Social Pressures in Informal Groups. Stanford. CA: Stanford University Press.
Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research. CBE-Life Sciences Education, 13(2), 167–178. doi:10.1187/cbe.13-08-0162 (full text)
Hanneman, R. A. & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside (full text).
In my last post about the #DALMOOC I described the course structure, its challenges and relected on the content. It is a nice structure but it does not contribute to readability. This time I will try a different approach. By pointing towards the three exclamation marks (in the figure below) as main themes of the post, I will interweave different resources, competencies and assignments.
The data cycle in data analytics
The data cycle adapted from Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist.
The data cycle is the main theme for week 2’s content of #DALMOOC. This data cycle (or data loop) consists of seven successive steps which are necessary to make learning data meaningful. Data Cleaning and integration can only take place after a potential data source has been identified and the data has been stored. The cleaning and integration part is especially important when it comes to combining different data sets as both need to be able to “communicate” with each other. The figure visualizes that the actual analysis of data is only the 5th step in the cycle – highlighting the importance of planning the analytics process. The action taken based on data relies mainly on the representation & visualization of it. By representing and visualizing them, data (or better: our analysis results) is “socialized” to ease the understanding of the analyzing results. [related to Competency 2.1: Describe the learning analytics data cycle]
No word on learning so far, but before diving deeper in the data cycle context I want to come back to my definition of learning analytics: “The field of Learning Analytics offers methods to analyse learners’ behavior in a learning environment and by this providing groundwork and significant potential for the improvement of learning environments, and individual learner’s feedback and learning outcomes”  The bold parts have been added with regards to Shum, S. B., & Ferguson, R. (2012) [Social Learning Analytics. Educational Technology & Society, 15(3), 3-26]. I adapted my definition as the term “potential” points towards the possible future development of (S)LA and “learning outcomes” as it adds a significant part to what needs to be improved: the result of a learning process.
Shum & Ferguson also introduce the interesting concept of Social Learning Analytics (SLA). It is based on learning theories and pinpoints learning elements that are significant in a participatory online culture. Primarily, they acknowledge that learners are not learning alone but engaging in a social environment, where they can interact directly or their actions can be traced by others.
While this is a new context for thinking about analytics, we need to understand the possibilities offered by SLA, which are either social itself (e.g. social network analysis, discourse analytics) or can be socialized (e.g. social learning disposition analytics, social learning content analytics). Moreover the challenge of implementing these analytics is still present. Shum & Ferguson emphasize the impact of authentic learning from real-world context through the use of practical tools. Important features of a participatory online culture are the needs for a complementary open learning platform (“digital infrastructure”), the understanding of how open source tools can enable people to use the potential of these tools (“free and open”), the importance of SLA as a part of individual identity and credibility of skills (“aspirations across cultures have been shifting”), SLA as integral part of an employee’s toolkit (“innovation in complex, turbulent environments”) and analytics as a new form of trusted evidence (“role of educational institutions is changing”). Social learning adds a particular interest in the non-academic context.
What is left are still the challenges of powerful analytics: which measures do we use? Who is defining them? What do we want to measure? How do we define access rights? Are we focusing too much on by-products of online activity? How do we balance power?
Data cleaning
Ealier I was claiming that usability is not always what we need. Shum & Ferguson are stating “User-centered is not the same as learner-centered: what I want is not necessarily what I need, because my grasp of the material, and of myself as a learner, is incomplete”. When it comes to start working with a data set it is highly important that it is as complex and complete as possible. If not the data set itself, there is no other element in the cycle that has the potential to embody the complexity of the problem. It is the visualization that simplifies and “socialises” the data, not the data cleaning nor the integration and analysis.
That automatically calls for “quality data” [Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist.]. Siemens introduces this idea (besides others) with a quote from P. W. Anderson “more is different”. In my opinion, this in one of the key elements of learning analytics and/or analytics in general: we are afraid of complex data because at a certain degree we are not able anymore to process them. Instead of refusing data sets based on complexity, good analytics can help us (as a “cognitive aid” according to Siemens) to process them and make sense of them. Because data will not stop existing – by refusing to handle them we make our lives easier but we might ignore potential sources to understand our lives better. We failed with classification systems so now time has come for big data and algorithm.Â
In addition to techniques of LA (as in Baker and Yacef 2009, adapted in my last post) Siemens mentions categories of LA applications. These are used for modeling user knowledge, behavior and experience. Thus, they can create profiles of users, model knowledge domains; perform trend analysis, personalization and adaptation. Siemens illustrates his point by giving the quote “A statistician may be more interested in creating probability models to identify student performance (technique), whereas a sociologist may be more interested in evaluating how social networks form based on technologies used in a course application.”
Data representation & visualization
There was a time when I had to work with SAP, data sets and visualization of these sets every day. And I am thankful for one lesson I learned during this time: Never change your data set to make it more illustrative. Keep the data set and base you visualization on it. That’s the reason I like Pivot tables so much. They have the potential to illustrate your analysis results, other users can adapt them and the data set will stay the same.
However, on has to keep in mind that analytic tools are programmed by others and to understand the way they work it is important to be familiar to the methods in use and how they are applied within such tools. During the DALMOOC we will work with different data analyzing tools. One of them is Tableau.
Tableau
[related to Competency 2.2: Download, install, and conduct basic analytics using Tableau software]
Tableau is a software that helps you in visualizing your data (based on business intelligence approaches). It has an impressive range of options for this purpose, which is (from my point of you) easy to understand and to apply. Data can be imported in different formats and besides common bar charts one is able to create “interactive” dashboards, an arrangement of visualizations that other user can adapt via filter and that can show you more details of the data-set on demand.
However, making data fit into Tableau can be challenging. The data sets have to be formatted in a certain way so the software can work with them. That was what I faced when going a little beyond #Assignment56 from the DALMOOC assignment bank “Finding Data”. [As the title says, find data sources that can you used in Tableau (you will need to watch the Tableau videos for this week in order to become familiar with this). Look for educational data on government, university, or NGO sites. Create a blog post (or post to edX forum) on these data sources.]
Coming from Germany, I did some research on the Open data movement there. The Bundeszentrale fĂĽr politische Bildung BPB (Federal Agency for Civic Education) offers an Open Data Dossier, where they describe the idea of Open Data, case studies and current running projects. In their words, the idea of Open Data is to make them available and usable for free. In this respect it is important to know the potential of data and data journalism for sustainable democratic development. Yet, this platform does not offer data sets itself but refers to a general framework for such a platform and to local pilot projects.
In this context they refer to the term “Hyperlokaljournalismus” that can be seen as opposite towards the classic idea of “Lokaljournalism”, offering very specific and detailed news in a dynamic way. They can be adopted to the location of the user and thus concentrate on immediate surroundings.
Three examples of Open Data platforms are daten.berlin.de, offenedaten.de and the “Statistisches Landesamt Rheinland-Pfalz“. Formats and range of data differ on each platform, but the idea is related to the statements of BPB: offer data for free and available for everyone. Nevertheless, browsing bigger institutions for data sets, it was mostly the visualization and not the data set that was available. For the data set, sometimes you had to download a form, sign it and describe the purpose you want to use the data for and then send it via telefax or e-mail. Why should I do this, when I am just looking for a data set for a individual analysis? I see the point that data collection can be very demanding and a researcher wants to protect his/her work. But when will we finally accept, that Open Data can contribute to a collective researcher community that works on data together? How do you enable easy data-check-ups if I have to send you an e-mail beforehand? And regarding the different data-set formats and integrating them in a certain tool: How long will we still need to clean data so they will fit in our analyzing tool? Will there be a future without the need to edit data formats? I do hope so.
Action
Although action was not a major theme in the course, I find this a very important part of the data cycle one has to consider. Particularly the action depends on data visualization so it is crucial to know whom we are visualizing our analyzing results for and what their needs are. This can be seen in organizational levels as well (“micro-, meso- and macroanalytics layers” according to Siemens) where we can have top-down and bottom-up approaches and the application of different questions and viewpoints. This emphasizes the directions LA can take and how they need to adapt according to the interest group they are serving. The main interest group – the learner and his/her need – however, might not be forgotten in this context. [related to Competency 2.3: Evaluate the impact of policy and strategic planning on systems-level deployment of learning]
Coming back to Siemens, he describes the challenges of LA (besides the data quality) as privacy and centered on human and social processes. I had the chance to touch upon the privacy topic during the Bazaar assignment, where I was in a group with him by chance. This again shows me the value of interactive tools when they are used in a suitable way.
Privacy is also one major topic Duval writes about in Duval, E. (2011, February). Attention please!: learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9-17). ACM. She describes machine readable traces for attention and raises the question what should be measured to understand how learning takes place. In regard of privacy she touches upon what elements should be tracked and if the user should be aware of this. She refers to the “Attention-Trust” principles of property, mobility, economy and transparency. The term “filter-bubble” is introduced, as connected to the idea that filtering per se can be a threat and can anticipate user choices and ideas. This is somehow related to Shum & Ferguson’s user-/learner-centeredness as it is always a question of who sets the filter and how do the filter work.
What is missing?
I would love to spend more time with working in Tableau and other tools in this course. But I fear I cannot cover this within the given timeframe. So I will focus on the tool matrix and complete it while using the tools for basics explorations, doing the readings and dealing with other course resources.
The below text has been handed in by me for the MITx course 11.132x “Design and Development of Educational Technology”. After some good results in the peer feedback and a very lifely discussion on the text with a friend I decided to publish it here as well, as I used some thoughts/input from my Master programme (especially for the conclusion).
Abstract
In this assignment I will first describe the blog as a current learning technology and the LĂśK (Lernen-Ăśben-Kontrollieren, german abbreviation for Learning-Practising- Controlling) as an earlier learning technology before I secondly conduct a comparative analysis of both technologies. My descriptions are enriched with personal learning experiences with theses technologies. I conclude with emphasizing that both technologies represent only one of several examples for the shift in learning theories over time and their impacts on educational technology. Additionally a major outcome is the claim for the critical evaluation of Ed Tech and the interdependencies between education and technology.
A current educational technology is the Blog (or Weblog, as a contraction of the words “web” and “log”), which can be described as a page on the web that operates as an individual accessible journal. Whereas in the earlier days of blogging knowledge about setting-up a homepage was required, Blog platform provider (such as wordpress.com or tublr.com) nowadays enable each individual to create a Blog without previous knowledge [Blood, Rebecca. “Weblogs: A History and Perspective”, Rebecca’s Pocket. 07 September 2000. 18 September 2013.]. When using this technology, the user’s main goal is to produce a text about a topic of interest and make it available for feedback.
Based upon this definition are the assets and drawbacks of this technology. On the one hand a blog can be used as a personalized learning technology adapted to specific needs to keep track of the learning process. Thus the technology’s goal is to offer a platform for keeping a learning journal for feedback purposes and continuous improvement. In addition, it operates as a sharing platform that is open to individual adaptation. However, in this openness lie the drawbacks as well. The technology does not  lead to a specific goal (producing a text is a broad goal) but rather is intended to be open-ended. By doing so, it requires a deep reflection on what the blog should be used for and how it can be used efficiently. Moreover, basic knowledge in producing text, expressing ideas, looking for appropriate resources and constructive feedback is required.
I want to illustrate these insights with the help of describing my first blog (franzidoesblog.wordpress.com) to support my individual learning process. It was set-up when I started my Master’s studies in Information Technology & Learning at the University of Gothenburg in August 2014. Whereas producing a text helps me to express my thoughts and reflect on them, once starting a blog can also generate pressure concerning how often to create a post and what to write about. The blog is useful for me to keep track on my progress and as it is open for adaptation I can implement it the way it suits my purpose. It helps me to improve my writing and reflecting competencies. Nevertheless, without the appropriate target group and range, getting feedback can be a hard task.
The LĂśK (or LĂśK-Kasten, www.luek.de) is a self-checking device for different developmental stages and ranges of subjects and topics (e.g. learning to read or mathematical calculations) [jayseducation.com]. It consists of up to 24 rectangle chips that can be placed in a flat box. Each chip is printed on both sides: one side with numbers, the other with coloured geometrical shapes. The inner life of the box is also printed with numbers (please refer to the above picture). Lately, it has been released as an App as well which follows the same operating principles (App release notice on luek.de [German only]). The intended primary audience are children from 2 to 13 years. It claims to be a learning system, that follows the latest research on learning. Even though I was working with an early version of LĂśK, it is still following the same principles today.
The operating mode is divided into three steps. First, there are guiding books for a variety of contents and levels of expertise that accompany the LÜK. In it, one can find the tasks to work through. Say one is working through task 1 that reads “Add 2 and 2!”. The result “4” has to be calculated mentally and then looked for within the chips. Once the chip with number 4 has been found, it has to be placed on the space in the box printed with 1 (representing exercise 1). If task 2 reads “Add 5 and 2!” “7” has to be found and the chip has to be placed on the corresponding space in the box for exercise 2. One important fact is that the numbers face upwards so that the geometrical figures can not be seen after the chips have been placed. Second, the box is been flipped over when all tasks have been solved. Third, once the box has been flipped, the geometrical figures face upwards and reveal the geometrical figure that has to be checked against the solution printed in the guiding book.
The LÜK resembles a sort of teaching machine. The tasks are divided into several progressing steps and once all are solved an immediate feedback is available. Furthermore, each student works on his/her own pace and is thus guided by an “individual teaching assistant”. The assets of this technology lie in the individual learning pace and the transparent instructions. However, this can be seen as a drawback as well. The learning process is clearly structured and by this can’t be changed. Though the pace might be individual the process structure (the sequence of tasks) stays the same. In addition, the LÜK does offer a variety of topics but the principle stays the same, making it predictable and prone to monotony.
I used the LĂśK-Kasten throughout primary school in the late 90s primarily for learning mathematical calculations. As far as I remember, most of the time I enjoyed it. We had to solve both – homework and in-class activities. However, sometimes we tended to switch to the geometrical figures directly and did a jigsaw puzzle instead of calculations by recreating the picture from the guiding book. The impact on learning was present, however seeing it from today’s perspective I doubt the effectiveness. Mostly because it was teaching to calculate without reflecting the task and you got used to the principle very quickly.
PART 3 – COMPARATIVE ANALYSIS
Comparing a Blog and the LÜK-Kasten is like comparing fire and water. Both are elements of the earth but besides this they do not really have anything in common. A blog and LÜK can be used to learn something but whereas the blog is giving a lot of freedom (blog-post as the only limiting factor), LÜK is providing the complete “learning process” (in quotation marks because I would not always call it learning process) including the content. Both approaches are thus very divergent.
To contrast the technologies, I want to try to make them more comparable by exploring instructions for the same developmental stage (I was using them at different levels of age) – let’s say at the age of 10 to learn a new language. Whereas following the instructions in a language class using blogs would lead to producing individual texts including research on it, LĂśK would lead to following instructions to answer questions and producing a new geometrical figure. One recognises that LĂśK itself is a very isolated and limited system compared to a blog. Each new content has to fit to the instructional idea of a guiding book and answering a set of up to 24 questions. These questions are limited in characters and context, there has to be one correct answer. Furthermore the content might change due to different guiding books but the geometrical shapes (the box itself) does not change. The blog does not give this strict guidance and thus needs the support by a teacher, that helps exploring and managing the technology for learning. But after this the blog offers a wider range of approaching a problem by creating an individual text that can be formated, enriched with pictures, videos and hyperlinks. Each post will be an individual artifact, that can be commented on by others and developed further. With each post the blog as an artifact itself is growing. This introduces a social aspect as well. Whereas LĂśK is a rather isolated learning process, a blog lives from feedback and improvement.
When it comes to engagement and motivation it appears as if the blog per se would work better. However, I am critical towards this attitude as especially a younger target group can be scared of writing independently about a topic, publishing a text online and making it available for feedback. But I see the teacher in a powerful role to overcome these obstacles. It might also be a familiarization with traditional behavioristic approaches that result in a certain assumption about learning. By saying so, I mean that children (especially in math) learn from the beginning, that there is always a certain process leading to one correct answer. Finally, this leads to a sceptical attitude towards new approaches such as a blog because they can be so different from what children were used to before. It might be that with the development of more cognitivistic and situative/pragmatistic-sociohistoric approaches in learning practise [terms taken from Greeno, J. G., Collins, A. M. & Resnick, Lauren B., 1996, Cognition and learning.] this might change.
To conclude, a blog can be more thought-provoking, memorable and playful whereas the LĂśK can be more structured and more motivating as it leads to direct feedback on the answers. Of course this is my personal view, it is highly likely that others would engage differently with these technologies. For example, one could enjoy the openness of a blog and using it for creative results. Another one could see LĂśK as being forced into a learning process that does not suit his/her preferences. All in all, both technologies derive from different learning traditions and mirror the ideas of their time.
PART 4 – CONCLUSION
Can we enrich learning environments with both technologies? If so, how? And even more important, how do we evaluate these technologies?
What becomes more and more important to me than answering these questions is the ability to critically evaluate technologies we (want to) use. Because technology is more than (just) a tool – it is not neutral. Neither does it have a sedulous impact. If it does have impacts we have to be more specific in describing them to not fall into the category of technological determinism [Oliver, M. (2011), Technological determinism in educational technology research: some alternative ways of thinking about the relationship between learning and technology].
In my school days it was important to critically evaluate historical sources and judge their context, purpose and credibility. However, when we talk about technology we tend to be more superficial. But technology – as historical sources – always has been invented and programmed by someone to fulfil a specific need. They do not appear from nowhere.
My above described examples are only one of several examples for the shift in learning theories over time and their impact on educational technology. By arguing, that it has always to be education that is driving technology we are missing the important point I scratched upon above: even if education is giving the primary direction for the development of technologies, it is education that has to be evaluated critically. Because both fields are fields of active and ongoing research – so many technologies which have been hyped are now at the edge to nowhere, and still we are launching into new technologies without taking a deeper look on where they come from, how they work and which needs they are supposed to fulfil.
After not having written a blog post for 14 days, I am becoming a little nervous: do I need to “produce” a certain number of posts per week, does the blog not work for me or do I not work as much as I was doing before? It might help to reflect on what I was working on during the last 14 days. I remember my lecturer saying, that writing down what you are working on actually can be a good way of visualizing it.
In terms of class activities the last few days were stuffed with preparations. Just now I realise how hard we were working on finalizing our seminar on B.F. Skinner’s “The Technology of Teaching” and a written assignment on an evaluation plan for an actual research project. We were working in the same group for both tasks, which was not always easy but in the end we managed to get great results with merging all our interesting input. What I really liked about both projects was a good time management and how we split up the tasks. Room for improvement on my side is a better reflection on requirements and individual workload as well as an even deeper analysis of literature.
Besides my own group work, the last days included two classic seminars from other groups on Simon’s “The Sciences of the Artificial” and Papert’s “Mindstorms” and several workshops and lectures. Especially the seminars were interesting as we worked on one selected topic as a whole group. Meaning that these days were prepared by a smaller group and they were to make the whole class work on it. A lot of discussions, a lot of resources, a lot of input – a lot to digest.
During the Wednesday group of academic writing we were working on a preliminary draft of our Master Thesis topic. That was not always easy, as you have to decide against so many options by deciding for another. In the end my draft includes three possible areas to look at:
How Data-Analytics can improve both: workplace training performance and workplace training quality/effectiveness
The definition of digital literacy and its implication for recruiting and personnel’s performance
Training methods lacking “new” input concerning new media use, training & education in a new context
I got some valuable feedback on my thesis statements that I prepared for each area and will work with this to specify my intentions.
Besides the class input I am working with several online platforms to complement the Master courses and/or get a different perspective on what I am learning. These platforms are Coursera, Edx and futurelearn.
I also took care of my application for the spring semester 2015. The deadline is October 15th and I found some courses that sound really interesting. My intention is to register for two courses in parallel to cover some credits for semester 3 already. This semester consists of 30 credits of choice and instead of taking them all in one semester I try to spread them over the preceding semester, so semester 3 would be available for some studies abroad.
What does work?
Class activities:Â Online collaboration and storage of data, blogging about learning experiences, take some inputs and apply them to online courses and viseversa.
Online Courses: After struggling beginnings it becomes easier for me to select appropriate courses and work through these parts that are important for my purpose.
What does not work?
Readings:Â I want to read more resources in addition to the course resources related to my interests/Master thesis.
The broader picture: A overall (technological) solution to display all resources, learning outcomes, etc. is not (yet) on-hand, rather I am trying to cover this by final assignments and/or transferring knowledge from one project to another.
More content: I feel that I am lacking the coverage of actual content at this blog, mostly I am writing about vague own opinions than well-grounded essays. This is also what I want to improve in.
All in all my midterm review is quite positive. I think that there always comes a time where you have to push yourself a bit to continue the good work. I am confident, that what I am learning is what I am interested in. Just this morning I was reading the next book for Wednesday’s classics seminar about Turkle’s “The Second Self” – and I am devouring it.
What is it that keeps us going? Is it our self-motivation or is it peer pressure? Is it both? Is it something completely different?
High aspirations can push you forward or hold you back. They can make you do better than ever and enjoy the things you do. They can make you feel deeply desperate about a world that does not share your ambitions and work-pace. Both perceptions notably occur when working in groups is involved.
How do you know, that you are doing well? Take an individual grading based on grading scales from 1-6 or 0%-100%. The basis is provided for continuous competition. Whether this grade says anything about how much you know or how well you are doing – it is a measure and it is recorded. For group work one could implement the possibility to opt for individual and group grading (meaning that individual contribution was made visible within the group project).
Switching to a Fail-Pass-Pass-with-Distinction system has been thought-provoking for me lately. Not about grading systems but about what and who defines others as doing well and how this influences individual/group behavior. Giving the pass-fail approach – I had an interesting discussion with two of my peers. How can we distinguish transparently between those that do good and those that do better? Is it simply enough to know that ourselves (how transparent is this then)? Or is it enough that the lecturer knows it and will keep that in mind for possible future PhD applications (if so, what is the benefit if someone does not want to stay in the research environment)? This might work if everything goes the way we expected it to be. But what if there is doubt and for a pass-fail decision there is no objective comparison between “competing” students at hand? Reflecting these questions it seems to me, that – independently of the grading system – we are focusing so much on the output of a learning process and forget the process itself.
In the business environment there seems to be a common agreement: you can only judge what you can measure. In the end you are responsible to proof what you did. And what you did not do. I tried to imagine a project evaluation by letting the project pass or fail. What would that change? Imagine there are clear expectations set but you can only pass or fail. There would be a hard separator between 49 and 51% (taking into account that these numbers would not be mentioned in the evaluation). So how would you consider one project over the other? By knowing it for oneself or believing in the project steering to keep in mind which project did better? By comparing the project records in detail? Again: this may work out well if we assume that people are (always)
good and they want you to do well,
objective and fair,
able to memorize the way work was done and can compare different projects unbiasedly.
But to be honest, this is not how real life always treats you – and as mentioned before – we are again outcome oriented. What really happens when it comes to group work is more complex than it could be measured by comparing final results. Take the social loafing phenomena (described as the loss of motivation when working in groups compared to individual work where work results are not merged): “hiding” in a group and not participating (because “the rest” will to the work) could be best prevented by setting an attractive common goal, increasing group identification and making each individual visible by avoiding incognito states (as we loaf because we expect others to do the same and try to preserve equity, because we feel anonym and not to be identified and because there is a lack of performance standard). I am not talking about output; I am talking about the group working process. On the other hand, there is this phenomenon of social compensation. This basically is the opposite of social loafing. People engage even more in group work because they know/assume/fear that co-workers/peers won’t/can’t work hard enough to reach the set goals. [Hogg, Michael A.; 2011] Once more, the route roote cause is the process of work, not the result. In addition, a very simplified second conclusion is that it is the individual recognition that counts towards connecting aspirations and our motivation to stay focused and on track.
To support the individual learner and the group outcome one solution would be to detect issues in the working processes before the result is presented. Definitely, we could rely on the group members to be objective and report such issues or on the lecturer/department leader to identify and sense tensions. But is this realistic taking into account the vast amount of technology used and communication around the project that is transferred to the digital environment? You cannot just trust your eyes and ears anymore, but you must be able to analyze patterns group members follow while using technological support.
In addition I am not talking about storing (intermediate) results (as I do it in this blog here for example). I am talking about the factual analysis of user data to identify patterns, workloads and routines. In anticipation on the critiques: yes, one could argue, that this is the control/observation/tracing of people by using information technology and data analyzing features. I want to argue that because people use information technology we need data analysis to acknowledge their work in the digital space.
Which leads back to the high aspirations mentioned earlier: just because I try to be as objective and unbiased about others and their work – may I assume that others try that is well? Should I set a good example and trust in others to evaluate the work done as a whole? If I do answer yes to all of these questions, does that mean I should not try to evaluate work more objectively? And when work becomes more complex, especially the progress until we reach a certain outcome, should I not use information technology (for the good cause) to improve my objectiveness?
I first came across the term “hackschooling” when watching the TedxTalk “Hackschooling makes me happy” by Logan LaPlante at TEDxUniversityofNevada from February 2013 (which has 6.1 Million views by now).
While doing some research on the term “hackschooling”, it struck me that there is apparently no (official) academic article published focusing on it. Is this because hackschooling fits perfectly within the unschooling/homeschooling classification or because it has not been recognized so far?
If it does fit – how does it fit? How is it related to these terms and where does it differ? How is hackschooling defined?
Due to a lack of academic resources, my attempt to answer these questions is based on grey literature such as blogs, videos and websites.
The method used: Initial Search / Browsing for resources
Since my university’s library search resulted in one source only [19], and google scholar made it to eight in total, from which I could use one [20], I turned to Wikipedia and Google.
Wikipedia does not list hackschooling. When entering this term in the search engine, it simply refers to “Homeschooling”.
Search result from wikipedia.org on 17th of September 2014 20:42
However, entering “hackschooling” on Google showed approximately 157.000 results. [Approx. 5.480 for “hack schooling”]
Search result from Google on 17th of September 2014 20:46
Would there be any resource available, which could offer a definition or try to put hackschooling into a broader perspective?
My prerequisites for browsing appropriate grey resources:
Term “hack schooling” or “hackschooling” in direct relation to Logan LaPlante’s Tedx Talk and
Hackschooling either defined and/or compared to other types of alternative education (e.g. homeschooling, unschooling etc.) [exceptions: 5+18+19; definition only]
A Definition of hackschooling
Watching the video of LaPlante already offers some starting points that have to be considered for a proper definition
Hackschooling is seen as the center of the four elements “Happy & Healthy / 8 TLCS*”, “Creativity/ Hacker mindset”, “Experimental classes & camps” and “Technology & Online Resources”.
*8 TLCs: Therapeutic Lifestyle Changes by Dr. Roger Walsh (Exercise, Diet & Nutrition, Time in Nature, Contribution & Service, Relationships, Recreation, Relaxation & Stress Management, Religious & Spiritual)
Hacking is described as innovative and as to challenge an existing system.
Hackschooling as mindset, not a system that can be used by anyone, even schools.
No particular curriculum nor approach, instead a network of people, a “re-mix or mash-up of learning“, focus on experiencing what one is learning and on hacks or shortcuts to get faster/better results
After the initial browsing, I selected 20 web sources based on the above criteria. Surprisingly, most of them only explain the term “hackschooling” very vaguely (if at all: by citing the video) and seldom relate it to other alternative education ideas (e.g. unschooling, homeschooling) by giving detailed information on why they mention this relation. The vast majority reports on the Ted Talk, only two are giving opinions and critical approaches to think about it.
Main findings
Out of 20 resources, a connection to unschooling could be found in two sources [1, 16], a connection to homeschooling in 10 [2, 3, 4, 7, 8, 9, 10, 11, 13, 15] a definition only in seven [5, 6, 12, 14, 18, 19, 20] and two sources did put hackschooling in a broader context [16, 17].
As for the definition, there were some sources reaching for more than already given by LaPlante’s talk:
Unschooling and hackschooling as different variations of homeschooling [3], but hackschooling is not restricted to homeschooling [2]
A concept in which education is open to being hacked or improved (by working within the current system and outside educational establishments) [4]
A way of creatively challenging traditional assumptions (more from a leadership perspective) and a benevolent form of hacking which includes hacking schooling [7]
A fresh take on homeschooling [9]
A home-school with a de-emphasis on “home” [15]
Hackschooling dedicated to unschooling [16]
“Hacking school” means something more than “adopting education technology” [18]
Hack schooling focuses on small diverse teams and meaningful learning and thus enhances social learning [19]
Hacker are challenging and changing the system so that it works in a better way, a mindset criticizing the actual education systems, mindset as a foundation to build a civic intelligence [20]
Open questions and what’s next?
First of all it would be pretentious to say, that the resources used represent the unitary point of view on hackschooling. Rather do they represent a point of departure for future investigations. But still – these first results mentioned above are leading towards some open questions.
Just because LaPlante comes from a homeschooling family, does this mean, hackschooling fits with homeschooling definitions? The most important next step will be an analysis of the definition of homeschooling and unschooling. How do these three terms “hackschooling, unschooling and homeschooling” relate to each other? For this more sound research on the terminology is needed. The following step will be the link to the broader perspective, which was already offered by some sources (incl. raised questions), e.g.:
Hugely lacking in supporting evidence, effectiveness? does the existing education system not already include/encourage creativity? arguing for something that already exists? [16]
Leaving open a lot of questions [17]
In conclusion, I am very surprised that no author tried to be more specific in what hackschooling is and where it can be placed within the educational environment. It might be, that this definition and classification is so obvious, that it is not worth mentioning it. It might as well be, that by leaving out lines of argument these resources miss an important point: defining hackschooling is harder than simply reporting on it as “just another form of homeschooling”.
Maybe this research was a waste of time – but this topic made me think and try to investigate something by using academic approaches. This blog post is thus more than just a post for me. It is also a way to work with the input given in the last few days of my Master’s program. I don’t know yet if this could become the foundation of my term paper. But I would appreciate feedback on my idea. 🙂
[Martin Kesselman , (2014) “On the horizon: The 2014 Consumer Electronics Show in Las Vegas, Part II”,Library Hi Tech News, Vol. 31 Iss: 4, pp.1 – 6]Â review of the TransformingEDU workshop
Summing up the first week of my Master’s programme, it was all about how we have come to the current idea of digital education and what underlying theories of learning and instructional technology paradigms relate to this development.
Taking a step back resulted in some reflections on my personal schooling history, it’s relevance for the person I am today and my concept I hold on education & learning.
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The hardliners of behaviorism
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Throughout school I was lucky to meet the hardliners of behaviorism (if one can say that) focusing on memorizing and recalling this memory (responding) in a particular occasion (to a stimuli). That resulted in me being able to …
recall all German federal states and their capitals without knowing where they actually are located,
being able to recite the multiplication table but struggling when working on complex math problems,
being able to cite Caesar’s Bellum Gallicum but not being able to translate one single Latin phrase properly.
I don’t want to say that this is solely an outcome of the way I was taught. But it becomes clear dto me how much influence schools do have on families (as they assume, that teachers know how to teach, and might copy their teaching style at home to support their children) and on the individual idea/concept of knowledge (if there has always been the one correct answer, I am likely to struggle in reality where there are mostly not one but several ways to answer).
Talking to a friend of mine about this he said “Well, but these are some facts, you could always get a good impression with.” Or as my old Latin teacher would put it: “Reciting Caesar’s Bellum Gallicum makes you the superstart of your regular’s table.”
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Aversive contingencies = punishment?
Browsing good old Skinner and “The Technology of Teaching” for my Classics seminar next week, I can find the keyword of ” aversive contingencies” or “aversive control” (describing an “unpleasant stimulant to change undesirable behavior” or in simpler words punishments such as extra work or detention [very happy that I missed the times of corporal punishment]) . Source
That keyword somehow describes my relation to school, as I was pretty often scared being in class. Especially when I knew that the teacher randomly assigned tasks to be solved at the blackboard (in front of the class) or presenting homework (as an impromptu speech). Whereas for other students that was a reward – for me it was hell.
What brings me to the importance of seeing students as individuals and recognizing their learning needs not only regarding content and pace, but regarding learning type and motivational aspects.
What for one student might be the most engaging activity ever, might be the scariest for another.Â
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Tracing the influences of the past
So this is how I have been taught mainly (although I have to admit, that especially during my A-Levels and my studies this schema of education vanished – and especially five years of working full-time have taught me that it is not important to know all facts, but to know where to can find them).
I can still find back some traces of this influence in my actual learning style and my assumptions of learning theories. I tend to …
think that there is alway this one correct answer,
have something done and then avoid developing it further (especially with feedback that comes too late)
think that I have to know it all by heart – which sometimes makes me lose track on my focus and get stuck with too many details.
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Making the best of it for tomorrow
Knowing this enables me to develop further. But what about education and technology in general? Reading about emerging technologies and how they can support the teaching process, there was one quote that I came across often:
“It’s nothing new what we are doing only because we use technology”.
For me it is, or to put it in better words, it should be and we should emphasize this. Please do something new with technology and please do not simply transfer your classroom lessons to digital environments. This is not what we need technology for.
To close with a quote from the Innovation Unit’s “10 ideas for 21 Century Education” report:Â
“Education systems have been resistant to change because education is so important – too important, some would argue, to experiment with. There is another way to look at this: in a rapidly changing world, education is too important to be left behind.” SourceÂ
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