“Conceptual Reverse Engineering” of Online Learning Objects and Sequences for Practical Applications

“Conceptual Reverse Engineering” of Online Learning Objects and Sequences for Practical Applications

Copyright: © 2019 |Pages: 16
DOI: 10.4018/978-1-5225-7528-3.ch004

Abstract

For instructional designers, one of the early steps in any design involves an environmental scan to see what publicly available online learning objects, sequences, and raw materials exist for the topic. “Conceptual reverse engineering” involves analyzing the online learning objects and sequences to infer how those objects may have been created, what technologies were likely used, the probable learning objectives, the apparent target audience, the prospective costs/inputs, and other factors. This information may be used to understand the state of the art, to inform a competing design methods, to inform the selection of technologies, to budget design and development work, to decide whether or not to adopt available third-party learning objects, and other applications. This chapter describes the creation of the conceptual reverse engineering of online learning objects and sequences (CREOLOS), which includes a step for validating/invalidating the reverse-engineered design.
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Introduction

A common analytical process in instructional design involves the conducting of environmental scans of online learning objects and sequences (and raw digital files) available online related to a particular subject or topic. “Conceptual reverse engineering” refers to the analysis of finalized digital learning objects and sequences and backwards inducing what went into the design and development of those contents. While “reverse engineering” generally refers to reproducing someone else’s product after examining its composition and construction, this is not about a full re-making but rather examining others’ learning objects and sequences in depth to understand how it was made—not to copy it…but to glean understandings. The “backwards induction” refers to starting with an object in its end state and analyzing and inferring how it may have come into being. This analysis involves analyzing a range of relevant features, and the information may be applied to various practical understandings and decision-making:

  • Understanding the state of the art.

  • Informing competing designs methods.

  • Informing the selection of technologies.

  • Enabling the budgeting of design and development work.

  • Informing decisions on whether or not to adopt third-party learning objects, and others.

This approach is conceptual in the sense that these are run initially as “thought experiments” and inferences about the designs. The next logical step in is to test the theorized designs by testing the learning object and its constituent parts; actually disaggregating the learning objects and sequences; reaching out to the original design and development teams; and reviewing raw files from the design and development. An assumed design is only that, and depending on who is conducting the conceptual reverse engineering and how much information is available, such a work can be fairly far off of accurate. That said, “conceptual” does not mean that the information is impractical or non-applied; as noted, this information may be applied to awareness, decision-making, budgeting, and other practical applications. The backwards induction analysis process of conceptual reverse engineering has not been systematized nor documented. Its (in)validity and strengths/weaknesses have not been assessed. This early work strives to lay the groundwork by describing this process and offering some analysis of it. The two hypotheses of this work read:

  • Hypothesis 1: It is possible to conceptually reverse-engineer online learning objects and online learning sequences to make some informative inferences about their design and development.

  • Hypothesis 2: The method for conceptual reverse-engineering online learning objects and online learning sequences may be somewhat systematized and described.

It is hoped that this may inform the analytical work and decision-making of others and that others may build on this work.

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Review Of The Literature

The term “learning object” was first used in 1994 by H. Wayne Hodgins when he named a working group for the Computer Education Management Association (CEdMA) “Learning Architectures, APIs and Learning Objects” (Polsani, 2003, p. 1). Learning objects may be digital, non-digital, or a combination of both, and a key feature is that they can be “used, re-used or referenced during technology supported learning” (Wiley, June 2000, p. 3). Online, there is a lot of duplicated learning information (Oliver, 2001), which suggests excessive “reinventing the wheel” and wasted design and development resources. The relative size of a learning object is important to its uptake, with the convention wisdom being that the smaller and more atomistic and basic an object is, the easier it is to use across a range of contexts; the larger the object (think modules, think courses), the less inheritable into existing learning sequences. Learning objects are conceptualized as fulfilling a list of so-called “ilities”:

Key Terms in this Chapter

Environmental Scan: A review of all available resources in the online ecosystem to understand the state-of-the-art.

Learning Content Management System (LCMS): A socio-technical system that enables the delivery of digital learning contents.

Federated Search: The ability to conduct a text-based search across multiple originating resources.

Virtual Immersion: The act or practice of spending time in 3D virtual immersive worlds for learning (simulations, foreign language practice) and socializing.

Repository: A database containing digital objects, usually based on particular applied uses and/or themes.

Order of Analysis: A sequence of exploration.

Conceptual: Reverse Engineering: A structured process of analyzing a digital learning object or sequence and interpreting what resources, costs, planning, and other elements were invested in building the object.

Findability: The ability to locate desired resources with efficiency and accuracy.

Accessibility: In the learning object sense, ensuring that all information is versioned in multi-perceptual ways for the broadest available human use (for example, image information is made available in text format, which is machine/screen reader- and human-readable).

Emic: The study of a culture based on its internal elements.

Non-Consumptive: The characteristic of not being destroyed during its usage (such as digital items that enable multiple usages).

Learning Object: A unit of learning expressed in digital, analog, or mixed digital-analog formats.

Referatory: An online site that points to various resources hosted on other locations on the web and internet.

Learning Management System (LMS): A socio-technical system that enables online learning, with common features such as the creation of persistent personal online accounts, intercommunications, small group work, group discussions, web conferencing, assignment delivery, grading, and other features.

Digital Learning Object: A unit of learning expressed in digital format (of various kinds).

Online Learning Sequence: The linear/non-linear (branching) experiences of designed learning paths in a digital learning object or online course.

Reusability: The ability to harness an object for uses in other contexts.

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