In the current ongoing work we propose the use of tracking and feedback mechanisms in order to improve our Personal Learning Environment (PLE), officially launched in October 2010. The approach can be seen as a necessary prerequisite similar to the darwinistic model of evolution. This means the implemented widgets will be improved (variation) and removed (selection) according to the observations. This paper will describe the backgrounds, methods and some details of the technical implementation.
Variation and selection are important mechanisms in the
evolutionary development of organismal life forms. These
mechanisms were extensively examined and described by Charles
Darwin in his famous book on the topic [
The interesting question is how evolution theory can help us in the development of a Personal Learning Environment (PLE). First of all the concept of PLE is still a new and vaguely explored concept. From an evolutionary point of view it could be considered as a new species conquering a still undetermined territory in an eLearning environment. There is no guarantee of success resp. survival of the species. More technical it could be considered as optimization process with undefined specifications how to solve the problem of helping the learner to overcome the challenge of managing distributed and potentially unknown but useful Web resources and Web applications.
The biological evolution would approach this problem by
choosing the r-strategy, which succeeds by a high (r)eproduction
rate. This strategy can usually be found when a species conquers
new space. In case of the PLE we need two different views on the
evolution metaphor, in order to fully apply this strategy. The first
view is macro evolutional, concerning the development of PLEs
as a ‘species’. The question here is about finding the most
appropriate form, which includes the programming language,
deployment, user interface metaphors and value within eLearning
environments (e.g. is it just a link list in an iPhone app or a full
grown web desktop). Therefore a long-time ‘survival’ of the
concept PLE would imply the development of many different
individual solutions in a short period time. The second view to
adopt is the micro evolutional view. In this view the functional
elements of a single PLE solution are considered be individuals,
struggling to ‘survive’ within the PLE. This view solves the
question of adaptation to the user’s needs on a functional level.
Which resources are really needed, which functions are necessary,
which are rarely used and which are never used?
A first prototype of a Personal Learning Environment (PLE) has
been developed and launched in October 2010 at Graz University
of Technology (TU Graz) [
In order to apply evolutional thinking, it will be necessary to establish the metaphorical links to the development of the PLE. The links will be mostly done on afore mentioned micro evolutional level, as this is more important to the specific development, however they can be adapted to the macro evolutional view easily. Evolution theory of natural selection uses the following relevant factors: reproduction rate and mortality (cycle for update, replacement and new widgets), population size (# of widgets), environmental capacity (max. # of widgets in the system and # of users using the widget).
In order to produce an evolutional pressure upon a population of
individuals, it is necessary to have a limited resource. In our case
there are actually two such resources driving the selection: a) the
limited space within the PLE UI and b) the limited number of
potential users. The first factor can also be described as growth
regulated and limited by population density, which is depicted in
fig.1. A population can’t grow unlimited, as there are limited
resources. The environment has a capacity, which is in our PLE
case represented by the maximum number of widgets.
The second factor b) can be operationalized as selection criteria
by asking the questions: ‘Which widget draws the attention of the
most users?’ and ‘Which widget has the biggest frequency of
usage?’ [
So the individuals of a population are forced into a constant competition for a certain resource against each other and against potential harmful conditions of the environment, producing variations for better adaptation. The different probabilities for survival are the base of the selection mechanism. Indeed selection is the main controller for the search direction within the evolutional optimization process. In biological systems it would Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
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determine which phenotypes reproduce at a higher rate.
Phenotype describes the amount of all observable characteristics
of an individual, expressed by its genes and influence from its
environment at a certain point of time. The natural selection is a
non-deterministic process, as it’s disturbed and interrupted by
random events. Individuals can die, thereby the evolution loses
information which could have represented an optimum solution
(e.g. the Wikipedia widget is dismissed because the company
offering the service wasn’t able to raise enough funds).
Environment and other contextual conditions are ever changing.
According to Solbrig [
The terminology of r/K-selection was defined by the ecologists
Robert MacArthur and E. O. Wilson [
Thereby increased quality come with a corresponding increase in parental nurture, while a focus on quantity would decrease the amount of parental investment. Each of the strategies is designed for specific environmental constraints. It is also possible that a species changes the strategy due to a change in the environment (e.g. the ecosystem becoming stable for period of time). However in nature many different mixed forms of these strategies can be found. In long terms the k-strategy will always be superior, which means that quality succeeds in the long run over quantity. The r-selection strategy (also referred to as r-strategy) succeeds in unpredictable, unstable environments. It is especially useful when it comes to conquer a new unknown ecosystem. It would be a waste of energy and time to adapt to circumstances which are still unknown and will most likely change again. Therefore the rstrategy is characterized by a high reproduction rate and short lifespan (see fig.5). Transporting this to the PLE would mean to provide a mass of functions (in our case widgets) without looking for quality in the first instance.
The K-selection strategy (also referred to a K-strategy) succeeds in stable, predictable environments and describes a growth which is ruled by population density, usually constant and close to the maximum capacity of the environment. The adaption process is slower but the lifespan is longer and it fills more effective the environmental niche. In case of a PLE the application of this strategy could mean the increase of quality of a single widget, due to several update cycles, thus adapting optimal to the user’s needs. In the example of our PLE we also use a mixed approach. With the beta ‘generation’ of the PLE a bunch of widgets was provided. These widgets were then tested for usability issues, corrected and deployed in the first generation. Last semester a class of students programmed additional widgets in order to produce a certain quantity of functions for the users. So the first and the second generation can be seen as mostly r-strategic. The update process will be repeated on an annual basis. Most of the students last semester chose to produce new widgets, while some reused and optimized existing code (which in turn can be considered Kstrategic).
The term variation in the evolutional context is usually described
as shift in the genotype or genomic sequence. These shifts occur
through a) mutation and b) recombination and generate new
phenotypes with different probabilities for survival.
a) Mutation is a random process, aiming only at the generation of
new alternatives. Mutation can result in different types of change
in DNA sequences. It can have either no effect, altering the
product of a gene, or prevent the gene from functioning properly
or completely [
Recombination is also referred to as cross-over. The process is working somewhere between mutation and selection, thereby combining and distributing genetic material (DNA, RNA) in a new way. There’s a random process determining the points where crossovers occur, however recombination is not a random process like mutation, as the recombination itself is not random. This means that the probability is low to separate genes that are close together or functional linked.
The code to all widgets in our PLE is open source and so far all widgets are open for variations by future developers. An open source policy and continuous development, resp. variation are a necessity for the ‘species’ PLE to finally succeed.
The basic architecture of the PLE is a mashup [4] of widgets. For each service a widget is provided that follows an extension of the W3C widget specifications [5]. The PLE, its requirements and its technological concept are described in detail in MUPPLE09 workshop [3]. Fig. 6 shows the general concept of the PLE as it is used at Graz University of Technology. The concept follows the idea to bring together university wide services with applications on the World Wide Web.
The implemented first prototype of PLE offers centralized access
to various University services [
There are many e-Learning services that are already provided by the TU Graz, including course administrations in TUGraz online, course learning materials such as e-books, podcasts etc. in TUGTC and user generated contents as well as user contributions such as blogs, bookmarks and files posts in TUGLL.
All these services are going to be integrated in the PLE as widgets. Therefore it was necessary to design a coherent GUI to avoid the possible usability and consistency problems that may occur [3]. The PLE GUI (see fig.7) is a combination of a traditional UI with a sidebar element and banner for orientation and navigation. In addition, it offers a widget-based UI with the so-called ”widget zones”, which require an adjustment by the user.
Widgets are categorized according to pre-defined topics. Each widget topic (category) has its own widget zones. The sidebar elements contain the main widget topics and help the user to switch between widget zones. The topics are easily extendible if the number of widgets is increasing. Furthermore, it is planned that the sidebar also updates the user on the status of the widgets by means of color and numerical indicators. The sidebar can be switched off in favor of the unfamiliar widget-based UI and replaced by another navigation element, which resembles the Mac Dock menu on the bottom, left, top or right part of widget zones.
The widget topics include different areas related to formal and informal learning, i.e. ”Communication Center” for emails, chats and news groups, ”TeachCenter” for all services related to the TU Graz LMS system TUGTC, such as course materials, podcasts etc., ”LearnLand” for services related to the TU Graz blogosphere system TUGLL social bookmarking, file sharing, etc. and ”Help and Support” for the help desk as well as Frequently Asked Questions (FAQ). These areas are called widget zones. Widget zones contain widgets and are structured in columns. The users can switch between widget zones, add, open, close, customize, position and arrange the widgets in different columns according to their personal learning preferences.
The widgets consist of a front side and a rear side, where the rear side contains the widget preferences that can be modified by the user. If preferences must be changed, the desired widget can be flipped. By this applied flip-animation the users spatial perception is undisturbed and makes the GUI more understandable. There are two kinds of widgets a) system widgets and b) standard widgets.
The users are able to create a mash-up of the most frequently used interesting widgets from different widget zones in a special interface called ”personal desktop”. The personal desktop is always available to the user and can be activated at any time. When the user activates the personal desktop it overlays the whole screen from the bottom of the page upwards (see figure 4.2 part 4). The user can add or remove widgets from all widget zones to his personal desktop and arrange them in columns according to his personal taste.
From the very beginning, an appropriate and good usability of the TU Graz PLE interface was one of the main objectives in the development process. Therefore during the implementation of the first prototype several usability tests were conducted, including heuristic evaluation and thinking aloud tests. The results were integrated and deployed in the current version.
Tracking user behavior, respectively the usage of individual widgets in combination with a feedback mechanism will provide empirical evidence for adaptive development.
Following an evolutional model of developing the PLE, this will mean a stepwise improvement and rejection of individual widgets in further iterations of the development cycle.
In order to improve the PLE we needed to consider different parameters that influence the attractiveness and effectiveness of the whole system in general as well as individual widgets. To meet this goal a tracking module was implemented to measure quantitatively how often the widgets are used and by how many users. The measurement was operationalized by the means of tracking individual and overall usage of widgets. In order to measure the usage of widgets a hidden module in the background tracked the users' active widgets.
The widgets that are used in PLE can be classified to three categories depending on how they interact with other services and applications on World Wide Web (WWW).
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from remote services such as RSS FEED reader widget. The client-side tracking module is added to the PLE widget engine to provide widgets including the possibility to offer information about user behavior on the client side. In periodic intervals the information (if any) is captured from all activated widgets in PLE and sent to the server-side tracking module for further processing. The server-side tracking module is used also for second and third widget types to capture information related to the user behavior in widgets depending on the data traffic on the server side.
At the current state of the PLE development there are 912 users in the system, whereof almost 30% can be said to use the PLE. In the last semester a group of students developed new widgets, in order to provide additional functionality as well as improving widgets from the previous beta stage. The system was introduced to the students in October 2010. The Tracking module was active since 1st of November 2010. At the current date this is 102 days.
First the acquired data seem not sufficient to draw any clean conclusions for improvement. As the feedback module wasn’t implemented yet, there is no chance of getting qualitative feedback, without performing another usability test. The analyzed data are purely quantitative. Nevertheless from the number of users, who have installed a certain widget, we are able to determine to top 5 used widgets out of the 30 provided. Actually theses top 5 are about the universities eLearning services, a mail widget and a system widget for changing the color styles of the interface (tugWidget, tccourses, tugllBlogs, mail, changeThemecolor). Within the top10 we find further a newsgroup reader, a game, google maps, facebook and the leo dictionary. From an educational point of view these choices make perfectly sense as these services are well known and frequently used even without the PLE.
Interestingly the most installed widgets are not necessarily the most used ones. The top 5 with the highest usage rate include weather forecast, rss reader, twitter, TUG library widget and again the leo dictionary. Within the top 10 we find here again google maps, Facebook and tugllBlogs, beside another dictionary and a currency converter.
Within the last update cycle, resp. the time when the students course developed new widgets, the weather widget was replaced by a new version. Actually this can be seen our current update strategy. If the outcome of the variation is a widget that fulfills a function better, then the old one will be replaced.
According to the hypothesis we expected to get more knowledge about user behavior, user preferences and derive data, which would help us to differentiate user behaviors, for instance between students of first and last semesters or students of different major of studies, and finally to improve the system in a natural way by variation and selection. However due to lacking qualitative data we are not able to falsify the hypothesis.
In order to gather qualitative measures of the user experience
(UX) in future versions, a rating system will be implemented. This
will be done either by a 5 star rating system or alternatively by a
small feedback questionnaire contained in every widget, which
consists of less than ten items of semantic differentials inspecting
the UX quality of the widget, respectively important variables of
evolvability. These would be attractiveness, dependability and
perceived effectiveness. The semantic differentials will be taken
from the reliable UEQ inventor constructed by [
In our PLE users can select some widgets from a widget pool and activate them for personal use. However if the user activates some widgets it does not necessarily mean that these widgets are actively used. In future versions the tracking module might be able to detect an active widget usage und track the usage in detail as deeply as possible.
In future works it would also be interesting to classify users according to their individual needs, for instance users who use more often only widgets with a strong focus on communication or users who use PLE more for learning issues, etc.
In order to meet data privacy considerations, we will implement a disclaimer, or terms of service (TOS) which needs to be agreed by the users once in order to use the PLE.
The tracking module provides sufficient quantitative data about the usage of the widgets. Bearing in mind that more knowledge about the learner will help in designing didactical models for providing learning courses, data gathering must be seen as a first valuable step. Furthermore these data combined with user profiles will be a precondition for building a recommender system on learning objects within PLE.