An overview of the papers
IRRODL (the International Review of Research in Open and Distance Learning) has once again produced a fascinating themed edition, this time about the application of information science approaches to online learning. The issue has been promoted, reviewed, and edited by a skilled team of researchers led by editors Dr. Maiga Chang (Athabasca University, Canada), Dr. Rita Kuo (Knowledge Square Inc., Taiwan), Dr. Gene Loeb (Center for Technology and Mental Health of Elderly, USA), and Dr. Bolanle Olaniran (Texas Tech University, USA).
I provide at the end of this post a very brief summary of the papers, to give some indication of the range of topics. As Terry Anderson, the journal editor in chief, says: ‘The issue is a bit more techie than our usual offering’, and the articles certainly warrant careful reading, but are well worth it. I will provide here my personal reflections on what the articles, taken as a whole, suggest for future developments in online learning, although it should be pointed out that although all the articles are looking at computer-based approaches to issues in online learning, within them they reflect a wide variety of positions on the role of computers.
Computers and teachers
First I should lay out my inbuilt bias or prejudice. I am very skeptical about claims that computers can replace teachers. However, if computers can – and can do a better job – they should. We should always be looking for ways to improve not only the quality of post-secondary education, but also its cost-effectiveness. One can argue about the level of investment needed, but given the challenges on a global basis, we should not ignore opportunities to stretch scarce resources – and in particular skilled teachers – further.
These articles in fact are very interesting in that between them they lay out different roles for (human) teachers and computers. In most of the papers, the role of computers or software is to enhance or make more effective the role of teachers, rather than replacing them; in other words, the information science approaches here are providing additional tools for instructors.
There are several reasons for this. Perhaps the most important is that many of the tools or approaches described here are still in the early stages of development. They are partly developing definitions, theories and new approaches, and partly testing them as prototypes. Teachers are still needed, to provide input, to validate and to test the prototypes. We don’t know if some of the approaches set out in these papers will eventually be feasible or will work when scaled up. Even if the tools do turn out to be effective and scalable, the authors often see these tools as requiring additional intervention or control by teachers, and this is likely to hold for a long time.
Another reason is the still very strong limitations of computing in dealing with semantics, meaning, context and complexity. Despite huge advances in computing power, developing ontologies or protocols that apply to the extraordinarily wide range of contexts and variables in which most learning occurs is extremely challenging. One way this is done is to break the challenge into sub-sets, with the rest left to the training, experience and intuition of ‘live’ teachers. Reading these papers, it seems that the sub-sets being dealt with, while helpful, are still somewhat on the fringes of the challenges faced in most learning contexts. However, they are a start, and several (for instance recommendation systems for identifying papers most helpful for a particular learning task) seem extremely promising.
The third reason why this remains such a challenge (although one that is the easiest to deal with) is the very narrow view of learning often held by the computer scientists who work in this field, who tend to focus (not surprisingly) on teaching as information transmission and retrieval, rather than on teaching as cognitive, personal and social development. One reason of course is that it is easier to develop ontologies for the former and extremely difficult for the latter. Too narrow a view of learning is an easier challenge to overcome because it should not be difficult to ensure that computer scientists and educators work together as equals in approaching the challenge of teaching and learning. While most of the papers in this edition did seem to embrace this broader approach to learning, some did not.
Nevertheless, I greatly appreciate IRRODL’s decision to focus on this area, because we do need to bridge the world of computer scientists and educators if the power of computing is to be wisely applied to education and training.
Implications of the papers – especially for MOOCs
Once some of these approaches are established and validated, their main value is that they can be scaled up. This is of particular significance to MOOCs. Currently the main challenge for MOOCs is to:
- find ways of automating learner interaction with materials beyond the level of checking that information has been retained
- provide contextually rich feedback on learning
- improve unsupervised peer interaction to ensure knowledge construction,
- avoid, detect and deal with plagiarism
- provide secure forms of authentic and valid assessment of learning,
all on a massive scale.
In these papers, we did see some of the ways in which these problems might be resolved, or at least a more general approach to dealing with large blocks of learners with few instructors. I suspect that over the next year or so, we will see similar developments being applied to the design of MOOCs. How effective such approaches will be remains to be seen, but there is promise and it certainly seems worth trying. I just hope though that those responsible for MOOCs will apply as rigorous evaluation protocols as are found in the papers in this edition. Let’s hope that this is at least combined with independence in the evaluation of MOOCs.
Which way for online learning?
Lastly, I’m wondering whether we will see two very divergent approaches to online learning, one based on very low cost or free teaching to massive numbers, drawing heavily on a computer science approach to teaching and learning, and another based on a more humanistic approach to teaching and learning, with smaller numbers and greater involvement of human teachers, and hence much more expensive, but still with greater focus than at present on hybrid and fully online delivery.
In my mind, I think (or rather hope) that it will be neither such extremes, but a mix of the two approaches. Good quality education is never going to be free for most people; there will always be costs. The human approach will also remain a core component for most education. But a judicious combination of computer science and humanistic designs and flexible delivery should enable high quality education to be delivered much more cost-effectively than it is at present. These articles are important milestones on this journey.
Summary of papers in this edition
Butakov, S. et al. (2012) Protecting Students’ Intellectual Property in the Web Plagiarism Detection Process IRRODL, Vol. 13, No. 5
This article suggests an architecture for plagiarism detection that protects the student IP by sending a randomized selection of content to a third party plagiarism detector.
Yu, P-T. et al. (2012) A Rapid Auto-Indexing Technology for Designing Readable E-Learning Content IRRODL, Vol. 13, No. 5
This paper presents an automatic method for detecting the changes in a PowerPoint based videoed lecture, and embedding this technology in an online course as an interactive component.
‘The fastest and easiest way to provide an adequate amount of e-learning content is to record teachers’ presentations in a classroom or studio and then directly put those recordings into a learning management system (LMS)’…..However, this kind of streaming data lacks flexibility and interactive capability. Therefore, a user-friendly interface is required to let students easily capture any segment of the recorded instructional videos’
The authors designed a mechanism of regular testing which requires learners to answer questions corresponding to pop-up information triggered when they click on an access point found by the indexing mechanism. Changes in the powerpoint slides in this case acted as the trigger for the access point.
Cheng, J.-S., Huang, E. and Lin, C-L. (2012) An E-Book Hub Service Based on a Cloud Platform IRRODL, Vol. 13, No. 5
This research project developed an e-book hub service on a cloud computing platform in order to overcome the limitations of computing capability and storage capacity that are inherent in many mobile devices. The e-book hub service also allows users to automatically adjust the rendering of multimedia pages at different resolutions on terminal units such as smartphones, tablets, PCs, and so forth.
Winoto, P., Ya, T, and McCalla, G. (2012) Contexts in a Paper Recommendation System with Collaborative Filtering IRRODL, Vol. 13, No. 5
The authors designed, developed and evaluated a recommender system (RS) that enables students to recommend papers that will facilitate other students in their learning. The RS was tried out on both ‘novice’ (undergraduate) and ‘experienced’ (post-graduate) students. The authors found that a multi-dimensional system that took account of different pedagogical factors worked better than a unidimensional RS based on ‘liking’.
Baldiris, S. et al. (2012) Searching for and Positioning of Contextualized Learning Objects, IRRODL, Vol. 13, No. 5
This paper focuses on two ways to increase the re-usability of learning objects (LO). The paper
‘promotes LO reuse by encouraging instructors to access distributed learning object repositories (DLOR) as sources of LO with diverse granularity that could be elements in a generated learning design. [The] proposal consists of two different parts: the distributed learning object metadata searching process (LORSE) and the micro-context-based positioning process (LOOK).‘
The authors found that to achieve a viable solution with these repositories, the object metadata (in the LO depositories investigated) needs to be refined. Metadata available in the involved repositories currently has limited information. This inhibits identifying the contextual relevance of a learning object for re-use in a learning design.
Wen, D., Cuzzola, J., Brown, L. and Kinshuk (2012) Instructor-Aided Asynchronous Question Answering System for Online Education and Distance Learning IRRODL, Vol. 13, No. 5
This paper introduces a question answering (QA) system particularly suited for delayed-answered questions that are typical in certain asynchronous online and distance learning settings. The authors propose a solution that integrates into an organization’s existing learning management system. They present how their system fits into an online and distance learning situation and how it can better assist supporting students.
Wong, W-K., Yin, S-K, and Yang, C-Z (2012) Drawing Dynamic Geometry Figures Online with Natural Language for Junior High School Geometry IRRODL, Vol. 13, No. 5
This paper presents a tool for drawing dynamic geometric figures by understanding the texts of geometry problems. With the tool, teachers and students can construct dynamic geometric figures on a web page by inputting a geometry problem in natural language. A preliminary evaluation of the tool showed that it produced correct dynamic geometric figures for over 90% of problems from textbooks. With such high accuracy, the system produced by this study can support distance learning for geometry students as well as distance learning in producing geometry content for instructors.
Nguyen, B-A., and Yang, D-L. (2012) A Semi-Automatic Approach to Construct Vietnamese Ontology from Online Text IRRODL, Vol. 13, No. 5
An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. The authors present a support system for Vietnamese ontology construction using pattern-based mechanisms to discover Vietnamese concepts and conceptual relations from Vietnamese text documents. The approach provides a feasible solution to build Vietnamese ontologies used for supporting systems in education.
Tierney, P. (2012) A Qualitative Analysis Framework Using Natural Language Processing and Graph Theory IRRODL, Vol. 13, No. 5
This paper introduces a method of extending natural language-based processing of qualitative data analysis with the use of a very quantitative tool—graph theory. It is not an attempt to convert qualitative research to a positivist approach with a mathematical black box, nor is it a “graphical solution”. Rather, it is a method to help qualitative researchers, especially those with limited experience, to discover and tease out what lies within the data.