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Publicly Available Published by Oldenbourg Wissenschaftsverlag April 5, 2017

Orientation and Navigation Support in Resource Spaces Using Hierarchical Visualizations

  • Sebastian Gross

    Sebastian Gross received his Diploma degree in Business Information Systems from Clausthal University of Technology. He joined the research group ‘Human-Centered Information Systems’ headed by Prof. Pinkwart in October 2011. In July 2013, he followed Prof. Pinkwart to Humboldt-Universität zu Berlin where he works in the research project ‘Learning dynamic feedback in intelligent tutoring systems’ (DynaFIT).

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    , Marcel Kliemannel

    Marcel Kliemannel studied computer science at Humboldt-Universität zu Berlin, and graduated in 2016. In his Master’s thesis he investigated techniques for visualizing knowledge spaces and strategies for supporting navigation in multi-dimensional visualizations.

    and Niels Pinkwart

    Niels Pinkwart studied Computer Science and Mathematics at the University of Duisburg-Essen, where he also completed his PhD in 2005. Since 2013, he is Professor at Humboldt-Universität zu Berlin where he heads a research group which investigates Computer Science in Education and Society.

From the journal i-com


In this article we investigate how orientation and navigation in (extensive) spaces consisting of digital resources can be supported by using hierarchical visualizations. Such spaces can consist of heterogeneous sets of digital resources as for instance articles from Wikipedia, textbooks, and videos. Due to easier access to digital resources in the Internet age, a manual exploration of these spaces might lead to information overload. As a result, techniques need to be developed in order to automatically analyze and structure sets of resources. We introduce a prototypical implementation of a visualization pipeline that extracts information dimensions from resources in order to group them into semantically similar clusters, and visualizes these clusters using two different visualizations: a treemap visualizing clusters and nested subclusters, and a rooted tree visualizing groups of semantically similar resources as subtrees. In a lab study we evaluated the two visualizations and compared them to two control groups. The results may hint to users’ better understanding of the resources’ underlying knowledge as compared to using typical approaches (e.g. web search results as list) when using hierarchical visualizations.

1 Introduction

In the era of increasing data sets (often summarized by the term “big data”) and social media, seeking and publishing digital (learning) resources has fundamentally changed. Using search engines or digital databases with millions of books and scientific publications means an almost unrestricted access to such resource repositories. Beside typical ways of publishing information, collaboration projects such as Wikipedia, blogs and social networks are popular ways for producing and publishing information which steadily increase the set of available digital resources.

Resource spaces are interrelated sets of resources. If someone wants to explore a specific topic, a resource space could be composed of articles from Wikipeda or blogs, textbooks, lecture slides etc. In order to efficiently work with such a resource space, a possible way is to analyze resources and to (partially) structure them considering semantic relations or logical dependencies.

In this article, we investigate the research question how users can be supported to orientate in and navigate through resource spaces. Based on (meta) information extracted from resources, we implemented two different visualizations that structure and visualize sets of resources considering relations among resources. In Section 2, we review the state of the art in visualizing interrelated information. We then sketch our approach to automatically analyze sets of resources in Section 3. We conducted a lab study in order to evaluate the approach. The results of the evaluation are summarized and discussed in Section 4. In Section 5, we exemplarily discuss how this approach can be used to help learner orientate in sets of learning resources in a web-based learning system for programming. Finally, in Section 6, we present our conclusions and give an outlook on future work.

2 State of the Art

In order to provide an efficient access to a resource space, a common way is to process and visualize its structures. In a meta-analysis, Nesbit and Adesope could show that using manually structured visualizations of learning resources leads to statistically significant improvements of learning achievements of learners [1]. Also, they found that concept maps, which aim at supporting learners comprehend contents and relations by manually creating and visualizing them, are associated with increased knowledge retention. Concept maps have been subject of study since decades [2, 3] and have been used in several approaches for supporting users to organize resource sets. Canas and Novak, for instance, developed the “CmapTool” which enables users to individually and collaboratively create concept maps and, thus, to manually structure resources [4]. CmapTool also provides mechanisms to suggest appropriate learning resources using web-based search queries. Webster is similar to CmapTool in facilitating individual organization of resources for reference and learning purposes by using concept maps [5]. A semi-automatic structuring has been implemented in the tool “Nestor” which visualizes traces of web-based search queries [6]. Users can also manually annotate these traces in order to emphasize relevant web resources. There are other approaches that help users process and visualize weighted search results [7]. Those static visualizations, however, do not support an efficient navigation and orientation through the visualized resource spaces. A resource typically has several dimensions of information (e.g. textual content and meta information such as author’s name, quality, etc.) so that the density of information increases with the amount of resources and users could be faced up with information overloading. Some approaches investigate how structures can be derived using user preferences or recommendations of other users [8]. In most cases, this information is not available from the beginning but needs to be gathered during usage. This limits the usage of automation since most of the approaches require complete data sets even from the beginning.

The severity of analyzing and structuring resources heavily increases with the increasing amount of resources. The efficient identification of relevant information and the dependencies between those can not be realized manually anymore at a certain point of analysis. In the field of text mining, there are several concepts that aim at automatically analyzing huge amounts of documents and structuring those documents into homogeneous groups [9]. However, these approaches mainly focus on identifying and revealing dependencies between resources instead of enabling users to identify and access relevant resources.

In this article, we, therefore, propose an approach that aims at visualizing spaces based on dependencies among resources in order to provide orientation in extensive resource spaces. Furthermore, our approach aims at enabling users to manually navigate to a space. Concerning this, we first introduce pipeline of techniques which identifies and derives a hierarchical structure of dependencies between resources. Each level of this hierarchy is annotated by terms which completely and compactly summarize the underlying subspace. For visualization, we implemented two different visualization tools which we compared in a lab study concerning their qualities to help users orientate in and navigate through resource spaces.

3 Analysis and Visualization of Resource Spaces

In our approach, the starting point of a resource space is an unstructured set of digital resources consisting of different formats (e.g. text documents, HTML source codes, PDF documents, audio or video files) and forms (e.g. text books, lecture slides, etc.). Based on the visualization pipeline of Card and colleagues [10], we propose a chain of techniques which automatizes the analysis and structuring of such unstructured sets of resources. The outcome of this approach is then used to provide a hierarchical visualization considering semantic relations among resources.

3.1 Analysis of Dependency Structures

Given a resource space about “software engineering”, this can be structured into subspaces such as “process model” and “software architecture”. The latter one can, again, be structured into “preliminary design” and “detailed design”. Given a set of resources, there is typically a natural hierarchical structure based on semantic relations within this set. Visualized as a root tree, resources could be represented by leaves. Semantically similar resources can be combined to a sub tree which will then be represented by an inner node of the tree. Those semantically similar inner nodes can then, again, be combined to a parent node. This operation can be repeated until the root of the tree is reached, representing the complete resource space. The resulting hierarchy has the property that each level of the tree has a certain level of detail regarding the contents it represents which accumulates from top to bottom. When a user is requested to navigate through such structures, she could reduce the set of resources which may be of interest for her by descending the tree from the root to underlying sub trees. Here, the inner nodes can give orientation which sub trees are related to the user’s objective. In order to provide appropriate orientation, those nodes need a condensed description of the underlying sub trees. This can be realized by extracting keywords from the underlying set of resources. For that reason, a resource needs to be analyzed and appropriate (i.e. meaningful) keywords need to be extracted from its content. We, therefore, derive the requirement that resources need to be compared semantically based on keywords which need to be extracted automatically.

Before we are able to conduct semantic comparisons of resources, the first step in the visualization pipeline should be to find a data model that makes resources from different sources comparable. Based on this model, we are then able to analyze resources and to identify semantic relations. A common way of semantically representing resources is using n-gram models [11, pp. 192–195]. Textual contents can be separated into word groups (also known as terms) consisting of n words which, then, represent the content of a resource. Since not all extracted terms from a resource are suitable to describe its content, terms need to be weighted in order to identify the most relevant of them. TF-IDF [12, pp. 8–9] is an appropriate method to identify the most relevant terms from text corpora by weighting terms that occur often in many resources lower, and weighting terms that occur rarely in resources higher. A subset of the most relevant terms can then be used to describe the content of a resource in a consolidated way.

After computing resource contents into comparable sets of terms, we are able to generate the second part of the visualization pipeline: generating a structure that considers semantic relations among resources. Using an agglomerative clustering algorithm [11, pp. 500–513], resources can be grouped into clusters considering their semantic similarity [13]. On the next level, similar clusters can then be grouped to new clusters until the top of the hierarchy (represented by a single cluster with embedded sub clusters) is reached. From intersection of related terms, we can, finally, derive a description for each cluster.

Figure 1 
            Resource space visualization using a rooted tree.
Figure 1

Resource space visualization using a rooted tree.

Figure 2 
            Resource space visualization using a treemap.
Figure 2

Resource space visualization using a treemap.

3.2 Prototypical Implementation of Resource Space Visualization

The next step of the visualization pipeline is to visualize the resulting hierarchy. There are different approaches to do this [14, 15]. From these, we have chosen two commonly used approaches: a rooted tree (illustrated in Fig. 1) and a treemap (illustrated in Fig. 2). The difference between both is that a rooted tree provides a front view whereas a treemap provides a top view on resources and their relations.

Both visualizations implement the principle of an interactive lense which enables users to show information on demand by moving the mouse cursor on a corresponding node. Moving the mouse cursor on a resource node of the rooted tree or treemap, respectively, a tool tip with detailed meta information occurs (illustrated in Fig. 2b). The same principle is used for supporting orientation in the hierarchical structure. As shown in Fig. 1b and Fig. 2a, a user can display those terms[1] which are related to a cluster node by moving the mouse cursor on the corresponding node. Upon clicking on a cluster node, the 50 most relevant terms are displayed in a panel of the visualization.

For the rooted tree view, we also implemented a semantic zoom [16] which displays the complete resource space on a low zoom level in order to enable users getting an overview, and, on each zoom level, the view refines itself and displays more detailed information (as illustrated from Fig. 1a with a low zoom level to Fig. 1b with a high zoom level).

4 Evaluation

We conducted a lab study in order to evaluate whether or not the approach proposed in Sec. 3 is suitable for efficiently and effectively supporting navigation through and orientation in resource spaces. For study purposes, we created a resource space related to the topic “software engineering” consisting of 137 resources from a set of digital textbooks (68), articles from Wikipedia (33), lecture manuscripts/slides (19), websites/videos (10), audio podcasts (4), and others (3).

The automatic analysis and structuring of the resources is based on the prototypical implementation as described in Sec. 3.1. The resulting structure was, then, visualized (as proposed in Sec. 3.2) using the JavaScript framework “D3.js”.

4.1 Study Design

Overall, 44 people participated in the study who were randomly assigned to one of the four conditions. We did not expect them to have any prior knowledge of the topic “software engineering”, and recruited them via Facebook and mailing lists for students from Humboldt-Universität zu Berlin. The four study conditions (2 experimental and 2 control) were designed as follows:


The first experimental condition used a visualization based on a rooted tree (as described in Sec. 3.2). The visualization could be adapted to users’ needs via filtering resources by search terms and preferences (e.g. display textbooks only).


The second experimental condition used a visualization based on a treemap (as described in Sec. 3.2). The visualization could be adapted to users’ needs via filtering resources by search terms and preferences (e.g. display textbooks only).


The first control condition used a file manager operating on the set of resources (as introduced above). Textual documents were provided as PDF files, videos and podcasts were made available as local files. Resources were separated into sub folders depending on their origin (e.g. from textbooks). Using a PDF viewer with built-in search tool, PDF documents could be searched.


The second control condition did not use the resource set but could use web-based search engines for searching the Internet for appropriate digital resources. All participants in this group were introduced to academic search using Google Books[2] and Google Scholar[3] to make sure they are familiar with the search tools needed for this study.

The objective of the study was to answer a multiple choice test (henceforth referred to as MCT) consisting of 31 questions related to 7 subject areas of the topic “software engineering”. We made sure that answers could be found in several kinds of resources (i.e. an answer was contained in an article from Wikipedia as well as in a textbook) and, also, that certain signal words from question texts were related to terms which could be found using the built-in search tools (this applies to both test groups and the first control group).

Overall, the participants had 80 minutes to use the visualization tools, file manager, and web-based Internet search, respectively, and to answer the 31 questions. Beforehand and after the MCT, we conducted a pretest (referred to as PreT) and a posttest (referred to as PostT), respectively, in order to determine participants’ prior and post knowledge on object of study. Both PreT and PostT consisted of 7 questions which were also used in MCT with slightly varying wording. PreT and PostT had to be answered without any kind of help.

4.2 Hypotheses

In order to investigate the research question, we stated the following hypotheses:

  1. Relevant resources can be found faster and more efficiently using a visualization in form of a rooted tree or a treemap. This leads to better understanding of the underlying knowledge because users are able to spend more time and pay more attention to contents. This effect can be measured by the difference between PreT and PostT results in both experimental conditions in comparison to both control conditions.

  2. A visualization using a rooted tree leads to a better overview on available resources and their hierarchical relations (as compared to typical visualization such as lists of search results) and, thus, enables users to more efficiently and effectively identify relevant resources navigating in the resource space from the root through the hierarchy down to resource representing leafs. This effect can be measured by a statistical difference in the number of search queries and the time spent with resources between both control groups.

  3. For users, it means more effort to identify relevant resources in both control conditions if navigation and orientation processes are not supported by hierarchical visualizations. This effect can be measured by the difference between time spent with resources and the system in both control conditions in comparison to both experimental conditions.

4.3 Results[4]

Due to non-normally distributed outcomes in all groups, we used non-parametric statistical tests. The percentage PreT, MCT and PostT results (in relation to total score to be achieved) and the statistical comparison of PreT and PostT results are summarized in Table 1.

Table 1

Percentage results of PreT, MCT, and PostT. Comparison between PreT and PostT results using Wilcoxon signed rank test for paired samples. Effect size measured using correlation coefficient r=zN.

RootedTree TreeMap FileManager WebSearch
min .0% .0% .0% .0%
max 45.45% 36.36% 72.73% 90.91%
avg 22.31% 22.31% 37.19% 37.19%
sd 13.70 13.70 25.54 22.06
min 10.94% 20.31% 12.50% 31.25%
max 43.75% 67.19% 67.19% 79.69%
avg 23.86% 41.62% 35.80% 55.40%
sd 10.03 14.81 19.97 14.51
min .0% 14.29% .0% 7.14%
max 50.0% 64.29% 57.14% 57.14%
avg 25.32% 31.17% 33.77% 31.82%
sd 13.31 19.76 15.35 17.30
comparison p = .53 p = .53 p = .66 p = .79
PreT/PostT sd = 11.23 sd = 11.22 sd = 11.24 sd = 11.25
r = .19 r = .19 r = .13 r = .08

Table 2 summarizes the pairwise comparisons of MCT results. Here, we found a statistically significant difference between the experimental condition RootedTree and the control condition WebSearch (p = .001, se = 5.47, r = .85).[5]

Table 2

Pairwise comparison of MCT results using Kruskal-Wallis test for unpaired samples and Dunn-Bonferroni’s nonparametric test for post hoc testing (significance level of .05). Effect size measured using correlation coefficient r=zN.

RootedTree TreeMap RootedTree FileManager RootedTree WebSearch TreeMap FileManager TreeMap WebSearch FileManager WebSearch
p .08 .65 .0 1.0 .85 .11
r .53 .34 .85 .19 .31 .50

Table 3 shows the (min, max, and average) number of answered question without regard to answer’s correctness. It is obvious that the control condition WebSearch performed best in this category, whereas experimental condition RootedTree performed worst. The difference between both conditions is statistically significant (p = .001, se = 5.45, r = .80).

Table 3

Min, max, and average number of answered questions without regard to its correctness.

Answered questions RootedTree TreeMap FileManager WebSearch
min 12 17 12 19
max 21 31 31 31
avg 16.64 23.64 21.09 23.64
53.57% 76.25% 68.04% 88.89%
sd 3.61 4.76 7.93 4.23

In Table 4, we summarize the number of search queries the participants conducted. The results are based on log file (RootedTree, TreeMap and WebSearch) and screencast (FileManager) analyses, respectively. There is a statistically significant difference between groups (p = .001, df = 3): The comparison between RootedTree and WebSearch (p = .001, se = 5.74, r = .81) as well as the comparison between TreeMap and WebSearch (p = .027, se = 5.74, r = .61) reveal a statistically significant difference.

Table 4

Number of search queries the participants conducted.

Search queries RootedTree TreeMap FileManager WebSearch
min 10 15 20 18
max 35 57 66 114
avg 21.45 28.09 37.09 61.73
sd 7.17 12.90 13.69 32.94
Table 5

Number of resources used by participants (in total and without doubles).

Resources used RootedTree TreeMap FileManager WebSearch
in total
min 29 28 25 64
max 104 111 78 162
avg 66.91 75.73 48.0 121.36
sd 21.02 26.12 14.55 34.74
without doubles
min 15 12 19 35
max 37 50 38 97
avg 27.09 32.64 28.18 68.64
sd 7.77 10.09 6.01 17.53

Table 5 summarizes the number of resources used in total and without regard to repeated requests of resources. Here, we found statistically significant differences between the control condition WebSearch and all other conditions.

Finally, in Table 6, we analyzed how much time participants spent with resources and on the system, respectively. The results are, again, based on log file and screencast analysis.

Table 6

Percentage time spent (relative to 80 minutes working time) with resources and on system, respectively.

Time spent with/on RootedTree TreeMap FileManager WebSearch
min 56.21% 50.74% 62.97% 75.72%
max 80.45% 72.67% 82.68% 97.59%
avg 70.30% 62.66% 70.76% 91.40%
min 19.55% 27.33% 17.32% 2.41%
max 43.79% 49.26% 37.03% 24.28%
avg 29.70% 37.34% 29.24% 8.6%
sd 8.16 7.19 6.84 6.69

4.4 Interpretation

We hypothesized in H1 that orientation in and navigation through a resource space can be supported by using hierarchical visualizations of resource spaces, thus enabling users to gain a deeper understanding of the underlying knowledge (represented by resources). We observed that participants in both experimental conditions RootedTree and TreeMap could improve their results (on average) from PreT to PostT whereas participants in both control conditions FileManager and WebSearch worsened (on average). Even though the results are not statistically significant at the .05 level, they show a tendency that hypothesis H1 may actually hold since the gain of users’ knowledge may hint at more consolidated mental models of the underlying knowledge and lower information overload.

The lower number of search queries in experimental condition RootedTree in comparison to experimental condition TreeMap tendentially confirms hypothesis H2. However, the difference between both experimental conditions is not statistically significant (p = 1.0, se = 5.47, r = .20)[6] and the MCT results indicates that a treemap better helps users identify relevant resources. This assumption is intensified by the following two observations: (i) Participants in experimental condition RootedTree (on average) answered fewer questions from the MCT than all other groups and (ii) participants in test group TreeMap, in fact, spent more time with the system itself but also used more resources (both overall and number of different on average).

Hypothesis H3, however, cannot be confirmed. While the number of resources which have been used (both in total and without doubles) is obviously higher in control condition WebSearch, this does not hold for control condition FileManager. Concerning the number of search queries, participants in both experimental conditions actually needed to use the built-in search tool less frequently but also requested more resources in relation to control condition FileManager.

In summary, it might be that participants of both test conditions could spent more time to face up with the resources’ contents and relations among them than let themselves be guided by search results. This assumption is tendentially confirmed by fewer search queries and improvements in scores from PreT to PostT. As stated above, this indicates that hierarchical visualizations (as proposed in Sec. 3) helps users develop a deeper understanding of the underlying knowledge and, thus, gain consolidated mental models.

5 An Example Application in the Domain of Learning Programming

We prototypically implemented (see Sec. 3) and evaluated (see Sec. 4) two different visualizations which use a hierarchy derived from semantic analysis (see Sec. 3.1) of resource contents. Even though the results are not clear as to whether or not the approach proposed in Sec. 3 supports users to navigate through and orientate in resource spaces, the findings give some reason to believe that this approach might lead to better understanding of the underlying knowledge and, also, might reduce information overload.

Since each session of the study was held over a period of 120 minutes whereof 80 minutes were spent to work on a multiple choice test, the study was not qualified (due to its short-term characters) to measure whether or not the approach is beneficial also in realistic longer-term learning scenarios where learners need to identify and use relevant resources and, based on these resources, acquire knowledge. Here, it is crucial to allow learners to focus on knowledge acquisition. Therefore, it is necessary to provide tools which reduce information overloading.

Based on the observed tendency that experimental condition TreeMap outperformed the experimental condition RootedTree, we decided to implement a prototypical visualization based on a treemap data structure in a learning system in order to evaluate the approach in a long-term experiment. In a research project funded by the German Science Foundation (DFG), we developed a web-based learning system for Java programming called JavaFIT[7] (formerly known as FIT Java Tutor [17]). JavaFIT integrates several pedagogical strategies to impart knowledge to learners: the system (i) allows learners to directly access and display learning resources (e.g. documents or video tutorials), and (ii) gives learners the opportunity to directly apply their knowledge in tests, quizzes, clozes, and programming tasks. For the latter, we developed an integrated development environment (IDE) which enables users to write, compile, and execute Java programs. A crucial aspect in (ii) is to provide feedback to learners while or after solving a problem. In case of tests, quizzes, and clozes we provide an overall score after solving a problem which indicates how well a learner acquired the underlying knowledge. For programming tasks, learners can request feedback to their programs while solving a given problem. This feedback is based on appropriate sample solutions. We, here, use machine learning techniques [18] in order to identify appropriate (sample) solutions automatically from structured solution spaces consisting of both learner and expert solutions [19]. Furthermore, learners can test their programs with predefined test cases which indicate how well learners’ programs solved a given problem, thus supporting learners to self-regulate their learning processes using the feedback the system gives.

As mentioned in (i), the system allows users to directly display learning resources. Overall, we provide a set of about 100 resources (as of Dec. 2016). In a previous version of the system, we provided a visualization based on a static structure of curriculum units (as it is typically implemented in learning management systems (LMS)) which was manually designed by a domain expert. However, this approach has several drawbacks. The underlying structure of the visualization has to be designed manually which means a lot of effort not only to create but also to maintain (in case of new learning resources) this visualization. Furthermore, the underlying structure does not necessarily reflect users’ needs. It might help learners navigate through a set of resources in a default way, but it does not support learning processes where learners want to determine their way through the space individually. And, finally, a static visualization (as we used in a previous version) does not allow learners to adapt the visualization by filtering or searching resources.

Considering the above mentioned drawbacks, we integrated an adaptive and adaptable visualization (as illustrated in Fig. 3) to the learning system which provides a hierarchical top view on resources similar to typical treemaps. Technically, we use the Javascript library 3D.js which provides several layouts for visualizing hierarchies[8]. In this case, we use a hierarchical pack layout which uses a treemap as underlying data structure. For structuring a set of resources into a hierarchy of resources, we use a similar approach as described in Sec. 3 where resources are semantically compared based on sets of terms. However, we do not extract these terms automatically (as proposed in Sec. 3.1) but we manually assign them to resources. This is caused by the fact that not every single resource has sufficient textual data which could be analyzed (e.g. video tutorials hosted on YouTube do not necessarily have descriptions nor keywords).

Figure 3 
          Visualization of a space consisting of learning resources. The visualization uses a hierarchical pack layout based on typical treemaps and can be adapted to learners’ needs by filtering and searching resources.
Figure 3

Visualization of a space consisting of learning resources. The visualization uses a hierarchical pack layout based on typical treemaps and can be adapted to learners’ needs by filtering and searching resources.

The visualization is based on semantically similar resource clusters. Due to the relatively low number of resources, we limited the depth of sub-clustering to 1 which means that sub clusters do not have sub clusters. However, with increasing sets of resources this could be changed in order to obtain a fine-granular visualization. The visualization can be adapted to a user’s need using filter tools (e.g. displaying video tutorials only) and provides a zoom which a user can use to zoom into relevant (sub-)clusters. We also implemented a hover effect displaying related terms and meta information when moving a mouse pointer on a cluster or a resource. For awareness and orientation purposes, we use different colors to emphasize those clusters whose related resources a learner already used and whose underlying knowledge she probably acquired. The coloring[9] is based on a learner model which records her progress and performance related to a learning resource.

6 Conclusion & Future Work

The evaluation results confirms the initially stated problem using resource spaces: the control group WebSearch used 64 resources in 80 minutes. This shows that a user who interacts with (unstructured) resource spaces as they are made accessible by search engines, is faced up with extensive sets of resources which need to be analyzed by the user itself. The results also indicate that using a web-based search engine might be efficient in terms of time spent on the system, but this does probably not necessarily lead to a better understanding of the underlying knowledge. This could be caused by information overloading where a user probably find relevant information but does not actually process and acquire them. Tendentially, the experimental conditions RootedTree and TreeMap better dealt with this issue which resulted in improvements from PreT to PostT.

We applied the approach in a learning system called JavaFIT in order to replace static curriculum structures by an adaptive and adaptable visualization which considers learners’ needs of individually organized learning processes. We are currently conducting a long-term experiment using a publicly available version of the learning system. Everyone who is interested in learning Java programming can register and use the system. We started the public instance in Sep. 2016. With this public instance, we are currently not conducting any controlled studies since its usage in the first months was relatively low, and thus we do not have any reliable results yet.

In upcoming studies, we intend to evaluate the helpfulness of the visualization by conducting a survey among registered users. Thus, the application in the domain of learning programming will provide us with a deeper insight of whether the approach is appropriate to efficiently and effectively support navigation and orientation processes in resource spaces. We are also going to address the problem of manually assigned terms by elaborating the proposed methods in such a way that resources with just a few (meta) information can be analyzed, too.

Award Identifier / Grant number: PI 764/6-2

Funding statement: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under the grant “DynaFIT – Learning Dynamic Feedback in Intelligent Tutoring Systems” (PI 764/6-2).

About the authors

Sebastian Gross

Sebastian Gross received his Diploma degree in Business Information Systems from Clausthal University of Technology. He joined the research group ‘Human-Centered Information Systems’ headed by Prof. Pinkwart in October 2011. In July 2013, he followed Prof. Pinkwart to Humboldt-Universität zu Berlin where he works in the research project ‘Learning dynamic feedback in intelligent tutoring systems’ (DynaFIT).

Marcel Kliemannel

Marcel Kliemannel studied computer science at Humboldt-Universität zu Berlin, and graduated in 2016. In his Master’s thesis he investigated techniques for visualizing knowledge spaces and strategies for supporting navigation in multi-dimensional visualizations.

Niels Pinkwart

Niels Pinkwart studied Computer Science and Mathematics at the University of Duisburg-Essen, where he also completed his PhD in 2005. Since 2013, he is Professor at Humboldt-Universität zu Berlin where he heads a research group which investigates Computer Science in Education and Society.


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Article note

This article is an extended version of the paper entitled “Orientierungsunterstützung in Ressourcenräumen mithilfe interaktiver Visualisierungen” by Marcel Kliemannel, Sebastian Gross, and Niels Pinkwart presented at the 16th Mensch & Computer conference 2016 in Aachen, Germany.

Published Online: 2017-04-05
Published in Print: 2017-04-01

© 2017 Walter de Gruyter GmbH, Berlin/Boston

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