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What does education need from ICT? It is clear we need to address how we extract knowledge from the Web, how we aggregate new with existing knowledge, how we deliver more current and essential knowledge, and how we can ease and improve assessment. All of these steps include ICT, the Web, and human involvement. This is called "Essential Knowledge Extraction, Aggregation, Delivery and Assessment." It is a practical pathway for considerable research in knowledge aggregation (using information processing support), extraction, and delivery. To succeed in delivering this aggregated knowledge we must understand how the learner's brain absorbs knowledge. In addition self-assessment supported and implemented by individually tailored (adaptive) assessment technology and tools can also improve knowledge delivery.
This aggregation can provide answers to the following questions:
First things first, what is really required? A simple Google search on "IT role in education" produces 1.5 billion hits; "curriculum design" gives 8.9 million; and "curriculum design models" yields a healthy 9.7 million. The numbers vary day-to-day, but the scale is impressive. In the last two years, MIT and Stanford offered software engineering and other courses online to which they enrolled thousands of students . Government statistics highlight massive open online courses (MOOCs) as a new learning option .
Unfortunately, digging a little bit deeper we find:
Thus, it seems there is much enthusiasm for online learning, however the success rates can be as low as single digits. Higher education curricula could benefit from the technological advantages of ICT [6, 7, 8]. Furthermore, keywords and their change and movement in any knowledge domain can be extracted and monitored using semantic Web methods [9, 10]. This opens up the possibility for support tools to automatically keep curriculum up-to-date. This involves monitoring a knowledge base, and recommending changes and upgrades for courses and materials when necessary.
Since the beginning of the 21st century, the efficiency of learning has actually deteriorated, particularly due to the quality and impact of ICT  . To counter this problem, information flow needs to be adapted and personalized to ensure effective knowledge delivery.
ICT platforms are changing rapidly; the modern user's preferred devices are smart phones and tablets. Screen size is reducing, thus putting greater pressure on humans and challenging interactive user interface design. Computing and database facilities are rapidly becoming remote and cloud based, while offering more power and security, but there can be issues such as delays and localized access restrictions.
ICT needs to be tamed and used systematically or it will fail to deliver. Achieving the potential benefits of productively combining knowledge formation, curriculum design, and redesign requires improvement in several areas including ICT involvement, such as:
Additionally, assessment techniques need to be modified. D. Patterson's keynote speech at WorldComp12 described how, out of more than 15,000 students recruited only 3,000 successfully completed the software engineering MOOC at Berkeley . This implies if there were two assessments for the module, say a coursework and an exam, a vast amount of assessment effort would have been wasted. In short, attempts to use ICT "as is" for knowledge delivery proved to be unsuccessful.
So can education do a better job and can eLearners achieve better results? We propose an integrated approach to curriculum design including the essential elements of:
To improve learning effectiveness it's always good to evaluate the amount of required repetitions it takes—keeping in mind our mother's plea "I told you hundred times and you still did not get it!" In practice the knowledge schema and its delivery process must take into account the nature and characteristics of the human brain and the channels through which information flows. This provides the basis for matching the speed and style of information delivery to the capabilities of eLearners. A birds-eye view of our integrated approach on Essential Knowledge Aggregation, Delivery and Assessment (EKADA) is shown in Figure 1.
Our thesis is knowledge delivery should be considered a single information processing system that includes the learners. The individual's means and ability to learn cannot be ignored; the brain's anatomy, physiology, and function—specifically how it learns—should be part of the scheme. A dynamically updated knowledge base must be accompanied by an adjustable delivery process, which, in turn, has to be adaptively tuned to an individual's capabilities.
In the past people had time to think, reflect, and react; they had time to use their associative memory and brain. Nowadays most people are swamped by information. To help people cope with this information avalanche, it would be useful to introduce schemes that semi-automatically identify, track, and adapt the changes and additions to knowledge as it develops and is detected. Web-semantics research can be used for educational purposes in two main ways: Firstly to detect, categorize, and extract knowledge; and then used by ICT to manage its delivery. Both of these processes are crucial given the sheer volume of information available now and emerging in the future.
Information technology could help us become even better or smarter, but due to the overload in our brains and the high dependency on our eyes, humans are already close to the limit in our ability to learn, filter information, and extract knowledge [5, 12]. At the same time, if the workload on the brain can be carefully balanced and adapted to consider knowledge assimilation and organization from the viewpoints of control theory, signal theory, and information processing, then our ability to absorb more and learn faster could be improved  .
The harmonization of knowledge delivery is based on the use of all possible information channels: textual, audio and visual. When activated, all three information channels tax the brain, each has a different level of information density, and all need to be used in concert to achieve the best outcomes for learning. Figure 2 illustrates connections between research paths of brain, perception, and efficiency of knowledge delivery.
As an example, the visual cortex deals with visual image sensing, while recognition and interpretation of "what we see" uses other segments of the brain. So, to an extent a person's ability to think while concentrating on the screen is actually reduced or even blocked. The perception of textual and audio information flow also contributes to knowledge formation in a learner's brain. To get quantifiable results of how eLearning impacts the learner's brain, the structure and processes of information delivery need to be researched in more depth.
There has been considerable research on the structure on the brain: Heinze et al's classic paper  , and recent results on this subject are impressive and should create a more efficient scheme of information delivery and knowledge acquisition  . The challenge is great; the size of the BigBrain model and aggregated data exceeds 29Tbyte and is growing  . While a model of this kind exists, experiments to incorporate new information and new links can upgrade the brain model and be applied in the areas where it is needed most—medicine, education, artificial intelligence, etc.—which might help us understand how the brain learns. The enormity and growth potential of such models present a great challenge for computer science in terms of structuring for both continuous aggregation of knowledge and the associative searching needed to mine the information required. To make this model really useful to knowledge delivery, two "live"experiments are needed to:
Combing research on how knowledge assimilation and curriculum delivery occurs in the brain could help make education more efficient, as Figure 3 illustrates.
Effective channeling of incoming information and investigating how knowledge is subsequently created inside the brain becomes a part of the field of knowledge delivery. Current activities, we believe, should be focused on:
Any lecturer will benefit enormously if the delivery of information is balanced for the best use of the audio, text, and visual channels. Remembering the poor attention span of students in the case of PowerPoint-based teaching , we argue that Stanislavski's work on the theory of performance demonstrated an interesting performance (knowledge delivery) can keep us present and attentive for several hours [15, 16].
Undoubtedly it is possible to maintain attention and create intensive processing of information absorption and knowledge formation when the circumstances are right: The material is relevant; it is presented in a way that is continually interesting and easy to assimilate; and the eLearner has the motivation to give it their attention, learn, and feel confident they are really learning and gaining the knowledge they need.
If considered together, both Stanislavski's and Gastón Bashlyar's [17, 18] systems engage the perception and attention of learners and could result in improved learning abilities that could also be applied to design, education and art.
The amount of text, its appearance, and format (including fonts, color, size, number of columns) have an impact on us, they depend on each other and our ability to immediately absorb/learn. Size of sentences, length and familiarity of words all make an impact. While the intensity of visual support and the subtlety of emotional delivery may have a strong negative or positive impact on our brain.
Text recognition is one of the fastest processes in the brain, it involves both primarily sensor groups—cones or rods for color and grayscale sensing. Words, grammar, and learned meanings within the brain activate associative memory and other areas, sometimes called "thinking schemes"  , in contrast to visual recognition ones.
Even though it is the easiest and most ancient scheme, there is no doubt text decoding is a significant energy overhead that could be better used for thinking. To understand how efficiently information can be processed into knowledge and how to combine delivery and thinking, a program of experiments is needed to determine:
Socrates said, "take as much democracy as you can carry," that applies here too. How can knowledge formation be analyzed using the independent variables of required power, volume, and time? And how much can we "carry?" Can the process of knowledge delivery be visibly improved?
The basis for the analysis of various channels is similar: The relevant segments and pathways of the brain involved in learning need to be studied. Followed by an evaluation of the chosen information delivery channels and quantification of energy required to process, understand, and assimilate information from the input data. The information flow can be measured and analyzed in terms of:
At a higher level noting "theories of performance" by Stanislavsky and Bashlyar's, one should think through combinations of audio, text, and visual information flows on knowledge delivery. It's time to stop dreaming that YouTube-like lectures can be a platform of effective delivery for education, unless they are based on a sound set of eLearning principles that take into account the physiological and psychological capacity of the learners [15, 16, 17,18].
To make knowledge delivery efficient, the delivery process needs further fine-tuning based on the innate ability to learn. For those who need extra support teaching within an ICT environment, shorter lectures, frequent tests and self-assessment schemes will be needed. For the more academically inclined the model of curriculum delivery and assessment needs to be different with higher levels of abstraction and self-study involved, supported by infrequent assessments and tutorial support when needed.
"At the lecturer frontline" a quantifiable model of workload including both the learner and the lecturer should take into account the range of abilities and prior knowledge of the intended learners. Clearly, such an ambitious goal is hard to achieve as time, effort, and funding are always limited. Therefore, a quantitative analysis and models of curriculum delivery might help to connect wishful thinking and reality.
Higher education institutions across the globe are using a variety of virtual learning environments (VLEs), Blackboard (now including WebCT) being a common platform. With more innovative VLEs appearing every day, presentations at conferences are booming. It all looks good on paper; databases are full, lectures and tutorials organized, and after answering endless multiple-choice questions students are told they have gained the best possible knowledge.
Again, in reality the picture is not quite so impressive as other factors have to be considered:
Use of ICT should ease the assessment phase, for example introducing a new multiple choice approach that ensures students address the relevance and dependencies between several key definitions of the topic in hand [6, 7, 8].
In reality, the rigorous formation of a set of essential questions—derived from the current curriculum—has been both elusive and difficult to attain. Keywords help to directly map possible knowledge structures, assist the process of delivery of knowledge, and make assessment almost automatic. Applying this approach reduces a lecturer's workload in creating an assessment and, at the same time, guarantees quality. Web-semantics research might help in the formation of an essential glossary and set of topic related assessment questions.
Using the Web for assessment has two major advantages: The learning process can be enriched with a training mode for self-assessment—becoming an integral part of learning, increasing self-assurance, and comforting a learner—as this can be done anywhere in the world and at any time. Secondly, during a defined and scheduled assessment time the student has much more confidence regarding the subject and process of assessment.
The "training mode" might also offer options for various depths and coverage of the assessment. In principle an unlimited number of questions on any given subject could be generated.
A new Web-based assessment tool is needed that is capable of continuously tracking the learner's knowledge, while progressing along the defined path with automatic monitoring and tuning of individual student knowledge and capability.
Through the work of our students the first version of an individual assessment tool (IAT) has been developed and released. Using IAT, you can create and run multiple choice answer (MCA) assessment design across a wide spectrum of high education disciplines .
Note the proposed MCA is based on different multiple-choice question approach where "one of n" answers is supposed. The psychological drawbacks of standard multiple-choice tests are well described by Roediger . Instead, MCA is based on the whole core knowledge set looking at the relevance of questions to various elements on a course, relations, and terminology with almost n/2 right answers from n proposed ones. As mentioned earlier, IAT helps self-assessment during the semester; devices such as phones, tablets, and PCs help lecturers monitor progress, and learners can seek relevant advice and pass the module as soon as they are ready. The first version of IAT was released for the Apple iPad (Figure 4 shows a screenshot). Our MCA approach was used to format questions and design an individual assessment tool, with support of the level of iteration of self-assessment and various penalty policies during individual self-assessment. This tool is called Teachflowapp and it can be downloaded for free from the Apple store.
Our biggest and fundamental problem is how to gain new knowledge, aggregating it with existing knowledge and efficiently deliver and apply it.
There has been a massive shift toward the use of eLearning platforms, as well as creating and using VLEs. Regretfully, so far use of ICT to transfer knowledge from the education provider to each specific individual consumer has not been as efficient as it should be.
We propose an approach that considers curriculum design formation and relevance—a process of its evolutionary change, delivery, and assessment—as one framework through several connected and dependent processes.
Semantic Web search techniques and platforms have become essential for trawling the vast expanse of data in the real world and then honing the results down into what is relevant and useful in curriculum terms for a given subject in a given field. This is a far cry from the rather rigid curriculum development committees, based on judgments from the limited experience of a few people, and academic assessment boards that dominate the current educational traditional systems. All of which inevitably slow down the reaction time of universities in keeping pace with change.
This research is driven by the belief that the way individuals learn is paramount for education to be effective. Understanding how knowledge is transferred in the brain makes it possible to design new delivery platforms that use all available information channels together in an effective way. Initially human audio, video, and text channels are being researched.
It is hoped the aggregation of brain studies with ICT supported curriculum design and development will increase the efficiency of education processes, primarily teaching, learning and assessment.
 Henn, S. Stanford Takes Online Schooling To The Next Academic Level.NPR. Jan. 23, 2012.  Evaluation of Evidence-Based Practices in Online Learning. U.S. Deparatment of Education. 2010.  Bacon, E. et al. Curriculum design, development and assessment for computer science and similar disciplines. FECS'12 - The 2012 Int'l Conference on Frontiers in Education: Computer Science and Computer Engineering, Las Vegas, July 2012. Paper ID: FEC4130.  E. Bacon, E. et al. Multiple choice answers approach: Assessment with penalty function for computer science and similar disciplines. International Journal of Engineering Education 28, 6 (2012), 1-7.  Bacon, E. et.al. Web-enhanced design of university curricula. In Proceedings of FECS'13, Las Vegas 2013. DOI: ISBN: 1-60132-235-6  Baroni, M., and Lenci A. Distributional memory: A general framework for corpus-based semantics. Computational Linguistics 36, 4, (2010), 673-721.  Patterson, D. Keynote speech. Worldcomp2012. Las Vegas. July 2012.  Amunts, K. et al. BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science 340,1472 (2013). DOI: 10.1126/science.1235381  H.J Heinze et.al. Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature 372, 8 (December 1994), 543-547.  Stanislavski, C. An Actor Prepares. London, Methuen, 1988.  Stanislavski, C. An Actor's Handbook: An alphabetical arrangement of concise statements on aspects of acting. London, Methuen, 1990.  Gastón, B. Dialectic of Duration. Cinamen Press Limited.  Gastón, B. The Poetics of Space. Penguin Group USA.  Roediger, H., and Marsh E. The Positive and Negative Consequences of Multiple-Choice Testing. Journal of Experimental Psychology 31, 5 (2005), 1155-1159.
 Henn, S. Stanford Takes Online Schooling To The Next Academic Level.NPR. Jan. 23, 2012.
 Evaluation of Evidence-Based Practices in Online Learning. U.S. Deparatment of Education. 2010.
 Bacon, E. et al. Curriculum design, development and assessment for computer science and similar disciplines. FECS'12 - The 2012 Int'l Conference on Frontiers in Education: Computer Science and Computer Engineering, Las Vegas, July 2012. Paper ID: FEC4130.
 E. Bacon, E. et al. Multiple choice answers approach: Assessment with penalty function for computer science and similar disciplines. International Journal of Engineering Education 28, 6 (2012), 1-7.
 Bacon, E. et.al. Web-enhanced design of university curricula. In Proceedings of FECS'13, Las Vegas 2013. DOI: ISBN: 1-60132-235-6
 Baroni, M., and Lenci A. Distributional memory: A general framework for corpus-based semantics. Computational Linguistics 36, 4, (2010), 673-721.
 Patterson, D. Keynote speech. Worldcomp2012. Las Vegas. July 2012.
 Amunts, K. et al. BigBrain: An Ultrahigh-Resolution 3D Human Brain Model. Science 340,1472 (2013). DOI: 10.1126/science.1235381
 H.J Heinze et.al. Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature 372, 8 (December 1994), 543-547.
 Stanislavski, C. An Actor Prepares. London, Methuen, 1988.
 Stanislavski, C. An Actor's Handbook: An alphabetical arrangement of concise statements on aspects of acting. London, Methuen, 1990.
 Gastón, B. Dialectic of Duration. Cinamen Press Limited.
 Gastón, B. The Poetics of Space. Penguin Group USA.
 Roediger, H., and Marsh E. The Positive and Negative Consequences of Multiple-Choice Testing. Journal of Experimental Psychology 31, 5 (2005), 1155-1159.
Igor Schagaev, Ph.D., Professor, FBCS, is with London Metropolitan University, author of System Software Support of Hardware Efficiency, holds the UK patent on Active System Control methods, published 80-plus papers in various journals and conferences. Igor's in-terests are in application of information processing and especially active system control methods for design and delivery of knowledge, gaining from new technologies and knowledge about human brain.
Brian Kirk, Ph.D., Managing Director of Robinson's Associates, UK. Above industrial de-velopment of electronics, software for transport control, verification of systems Brian has serious involvement in Web-enhanced design of curriculum developments for high educa-tion. Brian sits on several industrial panels of British universities adjusting curriculum and delivery process to industrial needs.
Liz Bacon, Professor, Ph.D., C.Eng., C.Sci., FBCS, CITP, FHEAPhD, Deputy Pro Vice Chancellor of Greenwich University, VP of British Computer Society, conducts and man-ages international research on eLearning, including recent FP7 EC project. Liz's interests are primarily in evolving curriculum design and new methods of Virtual Learning Environment.
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