Teaching-Learning with Interactive Visualization: How to Choose the Appropriate

Level? Mrinal Patwardhan1, Sahana Murthy2

1IDP in Educational Technology Indian Institute of Technology Bombay

Powai, Mumbai, India. mrinalpatwardhan@gmail.com

2Centre for Distance Engineering Education Program Indian Institute of Technology Bombay

Powai, Mumbai, India. sahanamurthy@iitb.ac.in

Abstract— Visualizations are proved to be beneficial in facilitating learning. Various parameters are used for classifying different types of visualizations. Interactivity level in visualization, as one of the parameters, encourages experimental mode of learning; especially useful in engineering education. This paper details the work-in- progress that offers guidelines for selecting an appropriate visualization based on its interactivity level for a given learning objective. The authors propose 'Visualization Selection Matrix'; a tool for selecting visualization, that maps cognitive levels and content types of a learning objective to interactivity level of visualization. The justification for the matrix is being offered in the context of a course on 'Signals and Systems'. The paper concludes with a brief outline of experimentation planned for validating 'Visualization Selection Matrix'.

Keywords— visualization; interactivity level; learning objective; cognitive level; content type

I. INTRODUCTION A visualization has been described as "the use of computer-

supported, interactive, visual representations of data to amplify cognition"[1]. As defined by Lengler and Eppler, it is a method of representation that helps in developing a deeper and clearer understanding of a topic [2]. Visualizations have been shown to facilitate learning, especially for abstract, complex contents that are otherwise difficult to observe. They allow learners to practice 'what-if scenarios', thus fostering their analytical skills [3]-[5].

Visualizations as instructional material come in different forms, from a static diagram, a video, an audio-video enabled animation, to an interactive simulation in which a learner can manipulate variables. Due to this wide range of visualizations, it has become crucial to find a common thread in order to group them. This grouping not only helps in categorizing visualizations, but also serves as an important criterion for determining an appropriate visualization, for a given purpose. The need and several methodologies of developing taxonomies for visualizations have been reported in [6]-[10]. Some of the parameters considered for classifications are: data type, display style, interactivity level, and task performed by user.

One of the important parameters considered [7], [9]-[13] while classifying visualizations is interactivity level in it. As reported in [14], interactivity has been defined as “a reciprocal activity between a learner and visualization based learning system, in which the [re]action of the learner is dependent upon the [re]action of the system and vice versa". The amount of interactivity in a visualization relates to the degree of involvement. It is based on various learning theory principles like behaviorism, cognitivism and constructivism that have evolved over the years [15]. These levels of interactivity starting at basic observing mode, progress till experimenting mode, thus taking the learner towards the concept of inquiry based learning. The interactivity level in visualizations allows users to observe interplay between various parameters of a system and how it affects system behavior in real-time. Among the various dimensions used for classifying visualizations, the interactivity level in it strongly encourages learners to perform experimentations while learning and helps in understanding intricacies of engineering concepts and their applications in depth. Thus, visualization with sufficient amount of interactivity offers benefits in technology enhanced engineering education.

Engineering curricula involve numerous tasks like knowing facts, applying facts and concepts, analyzing and designing complex systems, evaluating optimization techniques and so on; thus challenging different cognitive levels of students. Attempts have been made to establish a link between the cognitive level of a task in a visualization, and the interactivity level in it [6], [16]. As, a typical engineering curriculum contains a wide range of types of content (for example, listing quantities, understanding concepts, verifying principles, applying procedures), there is a need for establishing a link between types of content and visualization category in addition to the link between cognitive level and visualization category.

In the absence of clear guidelines, instructors often find it difficult to select the appropriate interactivity level in a visualization based on a specified learning objective. This paper suggests an approach towards selecting an appropriate visualization based on its interactivity level, for a given learning objective by mapping interactivity level to cognitive

level and content type. In this 'work in progress' paper, we propose a 'Visualization Selection Matrix' that offers guidelines for selecting visualization. The steps suggested for selecting the apt visualization are applied to topics from a course on "Signals and Systems".

II. RELATED WORK

A. Taxonomy of visualization Visualizations are beneficial while learning complex,

abstract concepts, dynamic phenomenon [5], [7]. Due to variety in types of visualization, the taxonomies developed for classifying visualizations serve as an important tool for researchers in understanding breadth of this field. A visualization classification scheme from the 'graphical point of view' and from the 'information-related point of view' has been reported in [7]. In the said paper, graphical representation of classification is done with the help of criteria such as dimension, representation, scale, time mode, and interaction while, information-related scheme involves intention, type of information, and information relationship for classifying visualizations. The basic visualization classification as 'scientific visualization' and 'information visualization' has been proposed in [8]. As described in [10], an overview of various taxonomies and a 'user-oriented and developer oriented classification framework' for visualization have been reported.

Interactivity level has been considered as one of the parameters for classifying visualization. Among the various definitions of interactivity considered [7], [11], [17]-[20]. Betrancourt has classified interactivity types as control and interactive behavior [11], while Choo has considered control, response, manipulate, and co-construct [20] as types of interactivity. Pahl has defined interactivity levels in terms of user's activity types that are based on the degree of involvement: observation, controlling, creation [18]. A more detailed hierarchy of interactivity levels in visualization has been discussed by Rolf Schulmeister [9], in which he has proposed six distinct levels of interactivity based on the similar past work by El Saddik [15].

B. Taxonomy of cognitive levels and content type Bloom [21] divided cognitive processes into six

increasingly advanced levels, where each level incorporates and goes beyond the previous levels. While retaining this framework, Krathwohl and Anderson revised Bloom's taxonomy and added one more dimension [22] in the Revised Bloom's Taxonomy: the knowledge dimension, in addition to the cognitive process dimension. Learning objectives written according to Bloom's taxonomy and its revised version are being used as a standard guideline for teaching, learning and assessing in various fields, and at various levels of instruction.

Another method of describing the interaction between a learner's cognitive performance level and content type is by means of the Content Performance Matrix proposed by Merril's Component Display Theory [23]. It classifies learning along two dimensions; content (facts, concepts, procedures, and principles) and performance (remember, use, find).

Similar to Merrill’s classification, Clark has classified content into five different kinds: fact, concept, process, procedure, and principle [24], and has further suggested different types of visual representational forms to present these different types of content [25].

III. RESEARCH QUESTION

A. Establishing need for research problem The need for linking different stages of learning to the type

of visualization has been reported in [26]. In linking cognitive level to interactivity level, higher interactivity level in visualization has been recommended while moving towards higher cognitive level [16]. While establishing link between content type and interactivity level, Clark [24] has confirmed association between content type and visualization required for its representation with different interactivity level.

One of the first steps in planning instruction and choosing appropriate instructional material involves writing learning objectives. Learning objectives for a topic related to engineering stream can be in the form of verb-noun pair in various combinations of cognitive level and content type, for example remembering facts, understanding concepts, applying concepts, analysing process, applying principles, creating procedures, evaluating principles and so on. The point that needs further exploration is how interactivity level in visualization can be mapped to cognitive level and content type for a given learning objective in the context with engineering education.

B. Research question and Scope of work Previous attempts in classifying visualizations, such as the

ones described above have focused on a single dimension such as cognitive level, or content type. While some of them have directly linked interactivity level in visualizations to one of these dimensions, as far as we know, there has been no attempt to simultaneously integrate different dimensions of planning instructional objectives into the process of selecting the appropriate visualization for instruction. Out of the various dimensions of visualization, interactivity level has a potential of offering experimentation facility to learners. Thus, in the context of engineering education, the process of selecting visualization on the basis of its interactivity level for a given cognitive level and content type needs detailed study. In this paper the research question we address is: How do we map the interactivity level in a visualization to the cognitive level and content type specified in a given learning objective?

On the basis of the discussion offered in subsection III-A, the process of choosing appropriate visualization has three dimensions; content type and cognitive level; forming two axes of this selection process, and interactivity level in visualization as the third axis of this process.

The scope of this work is being focused on certain topics from a course on 'Signals & Systems'. The abstract nature of the topics in this course, its application in many streams of engineering and its need as a fundamental, prerequisite course for any advanced course on Signal Processing have motivated us to consider this course for this work.

IV. SOLUTION APPROACH : FORMATION OF VISUALIZATION MATRIX

A. Defining interactivity level The hierarchy of interactivity level adopted for this work is

the one that has been proposed by Schulmeister [9]. The author has proposed six levels of interactivity numbered as 'Level I' to 'Level VI' with a comment that as 'Level I' might be considered as 'Level 0' as it offers no interactivity in visualization. Based on this, we have considered hierarchy of interactivity level (IL) starting from 'IL0' to 'IL5' that matches with Schulmeister's 'Level I' to 'Level VI'. The details are provided in table 1.

TABLE I HEIRARCHY OF INTERACTIVITY LEVELS CONSIDERED

Interactivity level (IL)

Details of interactivity level

IL 0 Viewing static picture, still images, no interactivity

IL 1 Viewing video, visualization that includes play, pause, stop, repeat, rewind, speed control.

IL 2 Permits manipulation of video display and viewing order (changing the order / sequence of viewing), zooming, rotating ( no change in content)

IL 3 Manipulating visualization contents through data input as in the specified range

IL 4 Generating visualizations through programs or data, such as software packages that allow users to create new models, mathematical equation. (Matlab, Simulink etc.)

IL 5 Receiving feedback on manipulations of visual objects ... associated with virtual /remote triggered labs for engineering applications

B. Interlinking of cognitive level, content type and interactivity level While revised Bloom's Taxonomy has offered two

dimensional table involving six cognitive levels and four types of knowledge dimensions [22], content classification done by Clark has suggested different types of representation forms while dealing with different types of content [24]. The content classification as done by Clark appears to be more appropriate when learning material needs to be presented in visual mode and can be linked further to interactivity level in visualization. In our work, we consider Clark's content type classification along with the revised Bloom's cognitive levels.

Thus, the process of interlinking interactivity level, cognitive level and content type leads to a matrix structure, which includes cognitive levels as its rows and content types as its columns. This matrix is intended to offer guidelines for selecting a visualization based on its interactivity level for a given cognitive level and content type of a learning objective. We refer to this matrix as 'Visualization Selection Matrix' as shown in Table 2.

The discussion from subsection III-A and III-B offers basic guidance for filling up entries of Visualization Selection Matrix. The need for higher interactivity level for higher

cognitive level and different interactivity level for different types of contents has been established therein. These guidelines have then been operationalized to decide the interactivity level of each cell in the Visualization Selection Matrix. Examples of such operationalized guidelines are mentioned below.

• Bloom's 'remember' cognitive level does not require interactivity in visualization as it is associated with recalling the already learnt matter. Thus, interactivity level associated with this cognitive level will be IL0.

• Content type 'fact' does not require interactivity level in visualization as they are basically considered to be discrete, static pieces of information. Thus, interactivity level associated with this content type will be IL0.

• It is anticipated that as we move towards higher cognitive level, interactivity level required in visualization should increase. Thus, 'evaluate' 'concept' will demand for more interactivity in a visualization as compared to 'understand' 'concept'.

• It is anticipated that a content of 'principle' type may demand for more amount of interactivity in visualization as compared to content type 'concept' while dealing with them at same cognitive level.

• While deciding interactivity level, necessity and sufficiency of interactivity level has been applied. e.g. if a given learning objective requires IL3 as per the steps explained here, a justification has been considered why IL2 will not be sufficient and IL4 will not be needed for meeting the given learning objective.

Thus, in order to select visualization for a given learning objective, we identify Bloom's cognitive level and content type from the learning objective, and refer to the recommended interactivity level from a cell of 'Visualization Selection Matrix' that corresponds to the specified cognitive level and content type.

A number of examples in Signals and Systems (for a given cognitive level and a content type) per cell were considered for validating the recommended interactivity level. In case of any mismatch in terms of recommended interactivity level (as per our operationalized guidelines) emerging from different examples taken for same cells, the examples were analyzed further for noting the reasons of disparity. When needed, the recommended interactivity level was revised so that it was consistent with the examples as well as the guidelines. The examples below illustrate the visualization selection process.

Example 1. 'Analyze' 'concept' Learning Objective: To compare sampled signals in time and frequency domain of a given signal when sampled at different sampling frequency. (The 'concept' is sampled signals and the learning objective expects learners to learn about under-sampled, over-sampled and critically sampled signals)

Explanation: This learning objective requires learner to compare signals when they are sampled at different sampling frequencies. Thus, the user should be offered complete control on variable manipulation. This requires IL3 (i.e. variable manipulation) as the minimum level of interactivity. Just observing visualization that offers interactivity level IL2 (i.e. only display manipulation, no change in content) will not allow user to observe change in the signal when sampled at different sampling frequency. Hence, IL2 is not sufficient for the said

learning objective. Though IL4 (i.e. user generated visualization) will allow user to create contents, it may unnecessarily complicate the learning experience. Thus, IL3 is the recommended interactivity level for the given learning objective.

Example 2. 'Analyze' 'principle' Learning Objective: To predict aliasing frequency for a given signal when it is under sampled. (The 'principle' is 'aliasing effect' wherein higher frequencies are masked by lower frequencies as a result of under-sampling of a signal).

Explanation: This learning objective expects learner to analyze principle related to under-sampling of signal and aliasing effect. As this content type 'principle' is a general rule, its analysis will require examples / models covering a range of inputs. Thus, limited manipulation of data by user as offered in IL3 will not be sufficient and will demand IL4 as the recommended interactivity level. As, IL5 involves feedback on the manipulations made, it would not be needed for just analyzing an already established 'principle'.

Example 3. 'Understand' 'process': Learning Objective: To explain shifting of a signal in time domain. (The 'process' is 'shifting of a signal in time domain', wherein signal is either advanced or delayed with respect to time axis by the time interval specified).

Explanation: A visualization in the form of a movie or animation that clearly depicts how a signal can be advanced or delayed in time domain will meet this learning objective. Hence, level of interactivity recommended is IL1 (viewing video with basic controls without any variable manipulation). A visualization of signal shifting will definitely offer better comprehension as compared to just merely watching a still image or static picture (IL0). As this specific learning objective may not demand facility in terms of rotate or zoom features, interactivity level higher than IL2 may not be required.

Example 4. 'Create' 'process' Learning Objective: To design mathematical modeling of any real life scenario (e.g. echo of an audio signal, a channel with multipath propagation) that uses signal shifting in time domain. (The 'process' involved here is shifting of signal in time domain which is present in phenomenon like sound echo, multipath propagation model).

Explanation: This learning objective expects users to develop mathematical modeling of any real life scenario that involves signal shifting in time. This learning objective will demand a visualization that offers creative features, thus demanding for minimum level of interactivity as IL4. If this learning objective expects learners to 'create' 'process' in lab environment, only then IL5 interactivity level offering visual / real time feedback will be required. Otherwise, the recommended interactivity level for this learning objective will be IL4. A limited variable manipulation as offered in IL3 will not be sufficient for this mathematical modeling.

The above examples exhibit variation in the cognitive levels and content types. It is worth noting that as cognitive level moves from 'understand' to 'create' for the same content type, interactivity level required in visualization also gets modified. Also, for the same cognitive level 'analyse' , when content changes from 'concept' to 'principle', the visualization requires different interactivity level.

C. Proposed 'Visualization Selection Matrix'

After considering rules and number of examples from different topics related to 'Signals and Systems' for finding out interactivity level of visualization required, the results obtained are summarized in the table given below. These recommended levels have been validated by taking numerous examples.

TABLE II. VISUALIZATION SELECTION MATRIX FILLED WITH PROPOSED INTERACTIVITY

LEVEL

Fact Concept Process Procedure Principle

Remember IL 0 IL 0 IL 0 IL 0 IL 0

Understand IL 0 IL1 or IL2 IL1 or IL 2 IL1 or IL2 IL1 or IL2

Apply IL 0 IL3 IL3 IL3 IL3

Analyse IL 0 IL3 IL3 IL3 IL4

Evaluate IL 0 IL5 IL5 IL5 IL 5

Create IL 0 IL4 IL4 IL4 or IL5 IL4 or IL5

V. DISCUSSION OF RECOMMENDED INTERACTIVITY LEVELS Generalised observations from the matrix are as follows: • IL1 or IL 2 is the level of interactivity required for

Bloom's 'understand' cognitive level. The interactivity required could be IL1 or IL2 depending upon the learning objective. Some content type may need a visualization with IL2 and may require change in representation form (in terms of zooming, rotating effect). The point that needs to be highlighted here is no change in visualization content is expected for this cognitive level. Thus, IL3 (offering user manipulated input) is not required at this cognitive level.

• IL 3 is necessary and sufficient interactivity level for Bloom's 'apply' cognitive level.

• IL 3 is necessary and sufficient interactivity level for Bloom's 'analyse' cognitive level for 'concept' and 'process', and 'procedure' content type. However, analysing a principles involve more generalization and different approaches. Thus, limited range of input and fixed input parameters will not be sufficient and will demand for user generated visualization of IL4.

• IL5 is necessary and sufficient interactivity level for Bloom's 'evaluate' cognitive level as it offers feedback in terms of visual objects from virtual laboratory/ remote triggered laboratory environment.

• IL 4 is necessary and sufficient interactivity level for Bloom's 'create' cognitive level for 'concept' and 'process' content type. 'Create' 'procedure' and 'principle' will expect either a pre-determined stepwise approach or formation of a rule or cause-effect relationship. Hence, a visualization generated feedback will be essential. Thus, 'Create' 'procedure' and 'principle' will need IL5 level for those domains that have visual objects/ associated hardware involved in the experimentation set-up or IL4 level if domain consists of only mathematical modelling.

This work has attempted to address the main research question of this paper: mapping of interactivity level with content type and cognitive level simultaneously. The recommended interactivity levels in the proposed 'Visualization Selection Matrix' clearly reflect learner's involvement and learning experience while moving towards higher cognitive levels and more complex content type. However, presently this matrix caters to course content of 'Signals & Systems'; which is a limitation in terms of its adaptation to other knowledge areas.

VI. FUTURE WORK: EMPIRICAL STUDY The authors have planned for experimental study for this

work. The establishment of necessity and sufficiency condition as applied to each recommended interactivity level will form the hypotheses to be tested. The impact of variation in interactivity level on students' cognitive level for a given content type will be main variable under study. The independent and dependent variables of the research question will be respectively, interactivity level and performance test scores involving questions catering to different cognitive levels. The sample will be chosen on random assignment basis from third year engineering students studying course on 'Signals & Systems'. In order to prove necessity and sufficiency condition of an interactivity level, each cell needs to be validated by considering visualizations based on the learning objective and appropriate interactivity levels.

Our first study will involve three groups which will receive different treatments in terms of visualizations with differing interactivity level for a learning objective in the 'analyse' 'procedure' category. Three groups will be asked to work with visualizations with interactivity level IL1/2, IL3 and IL4. The post-test results of groups receiving different treatments will be able to test necessity and sufficiency of interactivity level. Additionally, the pre-test planned will ensure matching of samples across the groups.

In the future, another issue that may be considered further will be the generalization of the 'Visualization Selection Matrix' in the context of other courses/fields.

ACKNOWLEDGMENT The authors would like to thank the faculty advisors and

research scholars of IDP in Educational Technology, IITB for their valuable comments offered for this research work.

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