NOT FOR REPRODUCTION OR DISTRIBUTION
Observing Interactivity in Learning Objects
Patricia McGee
Department of Interdisciplinary Studies & Curriculum and Instruction
The University of Texas at San Antonio
USA
pmcgee@utsa.edu
Abstract. Educational metadata designed to support reusability and accessibility also
reflects pedagogy, specifically through interactivity descriptors. This paper discusses
design influenced by epistemology, the nature of technology-mediated interactivity
and how interactivity may be interpreted and designed grounded in deeper learning
principles.
Discussion about, and the development and distribution of learning objects have exploded in government,
industry, and higher education. Although much has been written about what a learning object is (see Downes, Wiley,
and others), how they should be designed (see Dodds, Downes, Longmire, Wiley) and how they should be organized
and accessed (see IMS) there are still many unanswered questions, particularly in the area of pedagogical design and
utilization. By definition1 learning objects are designed to support learning and to be accessed and to be used more
than once. Earle (2002) stipulates that instructional design comes from epistemology and argues for a pedagogically
neutral approach to the design of learning objects that can be used with systems that are grounded any epistemology,
e.g. behaviorism, cognitive theory, constructivism, etc. Wildman and Magliaro (2003) believe that instructional
methods should be determined by content rather than learning theory building on what research reveals about
learning as behavior building (behavior), learning as mind building (cognition), and learning as membership
building (cultural participation). There is evidence that different disciplines approach instruction differently
(Donald, 2002) and these approaches may or may not reflect learning theory but rather a disciplinary-situated belief
system. This article briefly examines beliefs situated in metadata descriptors and explains how interactivity is a
pedagogically universal criterion for learning which may inform both the design and use of learning objects.
Educational Metadata
A good deal of learning object literature begins with references to technical standards such as Dublin Core,
IEEE, or SCORM since, as noted by Dodds (2001), conceptualization of learning objects focus on issues that are
embedded in systems. Metadata serves several functions most importantly to help an object be located and
functional within systems, as well as to denote ownership and permissions for use (Robson, 2001). Metadata
standards have been challenging to implement. Robson notes that the dilemma is twofold: locating the material and
the content design, which may limit practical usability. How content is presented may not reflect accepted heuristics
of the discipline or pedagogical expectations of the user (instructional designer, teacher, or student). Creating
metadata for educational elements that are universal in meaning across disciplines and learning contexts are open to
interpretation. For example, in higher education learning requires that an objective is achieved and that the learner is
assessed. Yet most educational metadata does not include attributes for evaluation, suggesting that documentation of
learning occurs outside of the learning object experience. One can argue that although metadata descriptors are
designed for identification and descriptive purposes, the more closely they reflect the language and epistemology of
the user, the more useful they will be.
Within the classification of metadata elements there are descriptors that clarify their meaning in a universal
manner so that a designer or instructor can discern which can be best combined in a learning sequence. The
educational category groups the educational and pedagogic characteristics of the learning object (LTSC, 2000).
Educational metadata elements vary between standard typologies (see Figure 1, An Example of Educational
Metadata Defined). Wiley (2000) argues that there is a lack of instructional design information that specifies
sequencing and therefore may facilitate a “clip art” approach to learning object use. The challenge is to embed
meaning that is clear to the range of experience and knowledge of the potential users.
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Although there are many interpretations of a learning object most agree that a learning object is an resources that
supports a learning objects and can be re-used in different environments,
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A common and highly visible concern of designers, instructors, learners, and theorists is the nature of
interactivity within a virtual learning environment. Metadata as a descriptor requires explicit meaning that is clear to
the potential end user’s (author, manager, learner, teacher) culture and understandings; shared operational
definitions2 that all parties find meaningful. IEEE descriptors for interactivity involve two components, type and
level. Type reflects pedagogy: active, expository, mixed, and undefined. Active interactivity indicates that the
learner is actively engaged with the content and possibility other learners. Expository interactivity means that the
learner proceeds through carefully organized material in a more a passive but efficient overview of content. A mixed
approach would combine both active engagement and an intentionally organized path while undefined interactivity
would not fit into any of the other categories. Level indicates a quantitative measurement: very low, low, medium,
high, and very high. A learning object may incorporate individualized feedback loops and data collection and
analysis along with interactive media and be considered high. However, others may define level in terms of
interactive actions alone. These concepts leave much up to the interpretation of the designer and have different
meanings in different settings.
Figure 1
An Example of Educational Metadata Defined
IEE LOM3 Descriptors
SCORM Classes
Interactivity type
Learning Resource Type
Active, Expository, Mixed, Undefined
Diagram, Exam, Exercise, Experiment, Figure, Graph, Index, Narrative Text, Problem Statement, Questionnaire, Self
Assessment, Simulation, Slide, Table
Interactivity Level
Very Low, Low, Medium, High, Very High
Semantic Density
Low, Medium, High. Very High
End User
Learning Context
Author, Learner, Manager, Teacher
Primary Education4, Secondary Education, Higher Education, University First Cycle, University Second Cycle,
University Post-Grade, Technical School First Cycle, Technical School Second Cycle, Professional Formation,
Continuous Formation, Vocational Formation, Other
Language
Typical Age Range
Difficulty
Very Easy, Easy, Medium, Difficult, Very Difficult
Typical Learning Time
Description
Reeves (2002) articulates pedagogical dimensions that relate directly to interactivity type. Epistemology
dimension that ranges from objectivism (knowledge exists outside of the learner), to constructivism (knowledge is
within the learner). The pedagogical philosophy dimension ranges from instructivist (learner as recipient) to
constructivist (learner as creator). The underlying psychology dimension is based on a learning theory continuum
behavioral (learner responds to stimuli) to cognitive (learner creates mental models). Interactivity type as illustrated
in Figure 1 reflects an instructivist and behavioral approach to learning that may not reflect the scope of learning that
does or can occur with learning objects.
Essentially, metadata attributes serve to describe how a learning object can serve as a commodity but do not
explain how learning objects support learning. The Educational Technology Expertise Center at the Open
University of the Netherlands has developed Educational Modeling Language (EML) that describes the types of
learning contained in learning objects and provides descriptors that allow for a range of epistemological systems
(Koper, 2001) however, use of EML is still limited.
Metadata of course does not and should not drive instructional design but if, as repository development
suggests, instructors or learners search for objects that may or may not reflect their epistemology or learning needs,
metadata may be the most direct way to identify those objects that reflect the preferred approach to framing content.
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interpreted and used in the same way by all users.
Institute of Electrical and Electronics Engineers Learning Object Management. . IEE uses SCORM Classes.
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For example, if a learner prefers or requires highly interactive content that can be manipulated as opposed to a
didactic presentation, metadata could be used to help differentiate between types of objects that meet this specific
need and enhance authentic interactivity.
Of metadata descriptors, interactivity relates most directly to pedagogy that may be embedded within a
learning object or in the environment within which the object is used. Learning theory informs us that to learn there
must be some type of interaction: between learner and content, learner and learner, or learner and instructor.
Technology-mediated communication adds yet one more type of interaction. The look and feel of technologymediated interaction is grounded in pedagogical design decisions.
Pedagogy
As online learning environments have become increasingly sophisticated, there has been a corresponding
focus on course and interface design that supports a more learner-centered experience (Thompson, 1998). Learning
environments should, it is argued, be designed so that they adapt to the learner rather than expecting the learner to
adapt to the environment. Most work in computer-based adaptive learning has addressed the needs of a wide range
of learning needs across a variety of content areas and focused on intelligent learning systems in which knowledge is
transferred from the computer to the learner (du Boulay & Goodyear, 1992; McCalla, 1992). Increasingly there is a
shift away form knowledge transmission to knowledge constructions (Derry, 1992; Jones, Greer, Mandinach, du
Boulay, & Goodyear, 1992), requiring support for cognitive processes (Woolf, 1992). In this approach, the
computer guides the learner toward understanding, soliciting metacognitive reflection about what they know and
understand. The system then can more authentically respond to the unique and individual needs of the learner
(Laurillard, 1992).
Agencies, institutions, and organizations that focus on the nature and delivery of distance learning promote
practices that reflect pedagogical beliefs about interaction. The American Distance Education Consortium (2001)
states, “Learning experiences should support interaction and the development of communities of interest” and makes
recommendations that course design should: foster meaning-making, discourse; move from knowledge transmission
to learner-controlled systems; provide for reciprocal teaching; be learner-centered; encourage active participation,
knowledge construction; be based on higher level thinking skills -- analysis, synthesis, and evaluation; promote
active learning; allow group collaboration and cooperative learning; provide multiple levels of interaction; and,
focus on real-world, problem solving. Although the IEEE metadata does describe learning resources which imply
constructivist behaviors on the part of the learner (e.g. simulations) these are not framed as interactive elements and
therefore require that to design for interactivity one must find a way to combine elements to provide interactivity
that supports knowledge construction. ARIDIANE and EML do a better job of articulating the type and degree of
interactivity but are still lacking in helping to describe how the object can best support deeper learning.
Although current discourse and literature all point to deeper learning as a desired outcome (see Carmean &
Haefner, 2002; Weigel, 2002; APA, 1997) what actually happens may not reflect these principles. We know little
about how faculty adapt to teaching within online systems and even less about how faculty pedagogy evolves over
time. The disparity between research-based principles and faculty or designer pedagogical beliefs may be deeper
than what we observe in actual practice. For example, IMS educational metadata descriptors point to an instructivist
and behaviorist approach to teaching and learning in contradiction to the deeper learning principles that reflect
active engagement with both content and other learners. Here two trends, both suggestive and influential in their
own right, are seemingly at odds with each other.
Hannafin and Hill (2002) argue that epistemology and psychology are embedded in the actions of the
designer, thereby situating pedagogy within a grounded design process that may result in a limited if not
inappropriate instructional design. Wiley argues re-usability of content-driven learning objects may not be useful or
even practical, not just for content reasons but for the learning processes embedded in them that may be
inappropriate or unsatisfactory in different contexts (e.g. considering the needs of the learner, content, or the
learning environment). Designer and faculty members may not be able or willing to create learning objects that are
pedagogically neutral which would increase their reusability but probably reduce their quality, if they are to satisfy
the principles of deeper learning that necessitate a breadth and depth of interactivity.
Pedagogical Interactivity
Pedagogical interactivity is derived from the application of learning theory to instructional strategy. Sims
(2000) analyzed elements of online interface interactivity through the four key component of any instructional
design: learner, content, pedagogy, and context. Although his findings isolated functions from a learning context,
they do illustrate that tools in of themselves and without conscious design are to some degree situated within
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specific learning theories. For example, a question-answer-feedback activity reflects a behavioral approach to
learning.
Moore (1993) defines interactivity in terms of pedagogy in which the learner interacts with elements or
people and is situated in the belief that knowledge is constructed through this interaction: learner-content involves
relating content to learner’s belief and knowledge: learner-instructor involves feedback and guidance to the learner
from the instructor; learner-learner involves processes that result in clarifications and knowledge construction;
learner-interface is a function of the technology system through which learner may interact with content, instructor
or other learner. From Moore’s typology, learning objects would fall into a combined category of learner-content
and learner-interface, but neither fall into either interactivity type or level as defined by metadata descriptors.
Yacci (1994) identifies specific attributes of instructional interaction by designating as a “message loop” in
between sender and receiver to sender. Interactivity is situated within the learner’s perspective so if there is no
response, interaction is not completed. The content of interaction must be “mutually coherent” with relevance and
meaning to the learner and, more importantly, interactions must not only constitute learning but also have affective
outcomes. Yacci suggests that technology adapts to the learner, a process that involves allowing the learner to make
decisions about interactivity and approaches to learning, supporting deeper learning principles.
Roblyer and Ekhami (2001) devised a continuum for identifying levels of interactivity in Web-based
learning environments that have implications for learning objects. In their approach, Web-based learning these
behaviors are instructor facilitated and provide student-student interactive experiences. Although perhaps not
applicable to learner-computer tutorials, the determinants for each level indicate a progression that is applicable to
levels of interaction. Going a step beyond Roblyer and Ekhaml, Bannan-Ritland, Harvey, and Milheim (1998)
combine type and level of interactivity in their model that describes six levels of interactivity in Web-based learning
environments: (1) Information Delivery; (2) Information Delivery with Pre-Defined Resources; (3) Information
Delivery with On-Line Interaction; (4) Pre-Designed Instructional Delivery; (5) Information Synthesis and Creation
of Resources; and, (6) Immersive Collaborative Environments. This model addresses elements that may be
incorporated into learning objects, if not in their current incarnation possibly in the future. The continuum, as in
Roblyer and Ekhaml’s model, helps to clarify what is low or high. Additionally, it is helpful to define the type of
learning environment or object. Interactivity is better described once the context of the technology-mediated
experience is given parameters.
Learning Objects and Interactive Design
The approaches to defining and conceptualizing interactivity in online learning provides a foundation for
conceptualizing interactivity within learning objects, regardless of their granularity. Earle’s (2002) idea of
pedagogically neutral learning objects, a complements Wildman and Magliaro’s (2003) idea that instruction should
not be limited to one belief system bur rather designed using research-proven principles that are appropriate for
content. To allow for the continuum of epistemology as described by Reeves (2002), the following rubric helps
identify how interactivity may be situated in a learning object. Others have identified how learning theory cans
guide the use of technology functions to support learning (see Dabbagh, 2003; Reigeluth, 1999) and these guides
should be used as a design tool rather than for reflective purposes for which this tool is designed.
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HIGH
Figure 2
Learning Objects Interactivity Observation Tool
Learner manipulates, alters,
or produces content through
active engagement and with
immediate feedback about
learning.
Technology is click and
move with linear navigation.
Leaner Interaction
Learner manipulates content
with periodic feedback and
progress.
Technology is click and
move with linear navigation.
Learner responds to prompts
and makes choices, with
periodic feedback about
progress.
Technology is click and
move with linear navigation.
Learner responds to prompts
with little or no feedback
about progress.
Technology is click and
move with linear navigation.
LOW
Learner is passive and clicks
through material.
Technology is click and
move with linear navigation.
LOW
Learner is passive and clicks
through material.
Technology provides nonlinear navigation, filesharing.
Learner is passive and clicks
through material.
Technology provides
production, automated
feedback and support file
sharing.
Technology Interactivity
Learner manipulates, alters,
or produces content through
active engagement and with
immediate feedback
Technology provides natural
and intuitive feedback,
manipulation, production,
publication, support, and
adaptation.
Learner manipulates content
with periodic feedback about
progress.
Technology provides natural
and intuitive feedback,
manipulation, production,
publication, support
Learner responds to prompts
and makes choices, with
periodic feedback about
progress.
Technology provides natural
and intuitive feedback,
manipulation, production,
publication, support
Learner responds to prompts
with little or no feedback
about progress.
Technology provides natural
and intuitive feedback,
manipulation, production,
publication, support
Learner is passive and clicks
through material.
Technology provides natural
and intuitive feedback,
manipulation, production,
publication, support, and
adaptation.
HIGH
The observation tool may be used within a relativist or instructivist design model and be expanded to reflect the
specific requirements of that orientation. For example, in an instructivist approach, feedback may direct the learner
to a prompt that gives immediate feedback while in a relativist system, the leaner may be prompted with questions, a
case, or other prompts that trigger deeper thinking.
Interactivity is a key element not only in learning but also in the design of all technology-mediated learning
environments. The tool described here may be used to not only observe learning objects that successfully support
learning but also to support intentional learning design to support interactivity.
References
ARIDIANE http://www.ariadne-eu.org/en/system/index.html
American Distance Learning Consortium. ADEC Guiding Principles for Distance Teaching and Learning. Retrieved
on September, 2001 from http://www.adec.edu/admin/papers/distance-teaching_principles.html.
American Psychological Association. (1997).
Carmean, C., & Haefner, J. (2002). Mind over matter: Transforming course management systems in effective
learning environments. Educause Review, 38 (1).
Dabbagh, N. (2003). The Intersection and Alignment of Learner-Centered Instructional Strategies in Online
Learning and their implementation using Course Management Systems. Presentation at NLII Next
Generation CMS, Tucson, AZ. retrieved on April 10, 2003 from
http://www.educause.edu/asp/doclib/abstract.asp?ID=NLI0330
5
NOT FOR REPRODUCTION OR DISTRIBUTION
Derry, S. (1992). Metacognitive models of learning and instructional systems design. In M. Jones & P. Winne (Eds.)
Adaptive Learning Environments: Foundations and Frontiers (pp. 257-286). Berlin: Springer-Verlag.
Dodds, P. (Ed.) (2001). Sharable Content Object Reference Model (SCORMTM), Version 1.2: The SCORM
Overview. Retrieved on May 23, 2002, from
http://www.adlnet.org/index.cfm?fuseaction=scormdown&flashplugin=0&cfid=238973&cftoken=2940108
7.
Donald, J. G. (2002). Learning to think: disciplinary perspectives. San Francisco, CA: Jossey-Bass.
Downes, S. (2000). Learning objects. Retrieved August 18, 2000 from the World Wide Web:
http://www.atl.ualberta.ca/downes/naweb/Learning_Objects.htm
Earle, E. (2002). Designing For Pedagogical Flexibility: Experiences From the CANDLE Project Journal of
interactive media in Education. Retrieved February, 2003 from http:// www-jime.open.ac.uk/2002/4/earle02-4.pdf
Educational Modeling Language. http://eml.ou.nl/introduction/explanation.htm)
Longmire, W. (2000). A primer on learning objects. ASTD Learning Circuits, March 2000. Retrieved August 1,
2000 from http://www.learningcircuits.org/mar2000/primer.html
(LTSC, 2000). IEEE Learning Technology Standards Committee.
du Boulay, B., & Goodyear, P. (1992). Student-system interactions. In M. Jones, & P. Winne (eds.) Adaptive
Learning Environments: Foundations and Frontiers (pp.317-324). Berlin: Springer-Verlag.
Hannafin, M. J., & Hill, J. R. (2002). Epistemology and the design of learning
environments. In R. A. Reiser & J. V. Dempsey (Eds.), Trends and issues in instructional design
and technology (pp. 70-82).
Hamel,C. J., & Ryan-Jones , D. (2002). Designing instruction with learning objects. International Journal of
Educational Technology, v3,n1 [ISSN 1327-7308]. Retrieved on January 24, 2003 from
http://www.outreach.uiuc.edu/ijet/v3n1/hamel/
Jones, M., Greer, J., Mandinach, E., du Boulay, B., & Goodyear, P. (1992). Synthesizing instructional and
computational science. In M. Jones & P. Winne (eds.) Adaptive Learning Environments: Foundations and
Frontiers (pp. 383-401). Berlin: Springer-Verlag.
Koper, R. (2001). Modeling units of study from a pedagogical perspective: The pedagogical meta-model behind
EML. Retrieved June, 2002 from eml.ou.nl/introduction/docs/ped-metamodel.pdf
Laurillard, D. (1992). Phenomemographic research and the design of diagnostic strategies for adaptive tutoring
systems. In M. Jones, & P. Winne (eds.) Adaptive learning environments: Foundations and frontiers (pp.
233-248). Berlin: Springer-Verlag.
McCalla, G. (1992). The search for adaptability, flexibility, and individualization: Approaches to curriculum in
intelligent tutoring systems. In M. Jones, & P. Winne (eds.) Adaptive learning Environments: Foundations
and Frontiers (pp.91-122). Berlin: Springer-Verlag.
Moore, M. (1993). Theory of transactional distance. In Desmond Keegan (Ed) Theoretical Principles of Distance
Education, London & New York: Routledge.
Reeves, T. (2002). Evaluating what really matters in computer-based education. Retrieved October, 2002 from
http://www.educationau.edu.au/archives/cp/reeves.htm.
Reigeluth, C.R. (1999) Instructional-design theories and models, volume II. Mahwah, NJ: Lawrence
Erlbaum Associates.
Roblyer, M. D., Ekhaml, L. (2000). How interactive are your distance courses? A rubric for assessing interaction in
distance learning. Paper published at the Distance Learning Association Conference, Callaway, Georgia.
Robson, R. (2001). Pedagogic metadata. Interactive Learning Environments, 9 (3), 207-218. Retrieved July, 2002
from http://www.westga.edu/~distance/roblyer32.html
Sims, R. (2000). An interactive conundrum: Constructs of interactivity and learning theory. Australian Journal of
Educational Technology, 16 (1), 45-57.
Sims, R. (1997). Interactivity: A forgotten art? Computers in Human Behavior, 13 (2), 157-180.
Standard for Learning Object Metadata Learning Technology Standardization Committee of the IEEE. New York,
NY: the Institute of Electrical and Electronics Engineers, Inc. Retrieved June 2002 from
http://www.scorm.tamucc.edu/scorm/course/metadata/beginner/assets/IEEELOMv6p1.pdf
Thompson, M. M. (1998). Distance learners in higher education. In C. Gibson (Ed.) Distance learners in higher
education: Institutional responses for quality outcomes. (pp. 24-29). Madison, WI: Atwood.
6
NOT FOR REPRODUCTION OR DISTRIBUTION
Weigel, V. (2002).
Wildman, T. M., & Magliaro, S. G. (2003). A half-century of progress in understanding learners and learning:
Considerations for effective utilization. Paper presented at the annual Meeting of the National Learning
Infrastructure. New Orleans, Louisianna.
Wiley, D. (Ed.). (2001). The instructional use of learning objects: Online version. Retrieved June, 2002 from
http://www.reusability.org/read/.
Woolf, B. (1992). Towards a computational model of tutoring. In M. Jones, & P. Winne (eds.) Adaptive learning
environments: Foundations and frontiers (pp.209-232). Berlin: Springer-Verlag.
Yacci, M. (1994). A grounded theory of information-rich learning environments. Paper presented at the Annual
Intentional Conference of the Association for the Development of Computer-based Instructional Systems.
Nashville, TN.
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