CHALLENGE SUMMARY
While the age in which we live has been termed the Age of Information, it also seems self-evident that the system by which we educate our youth is failing to produce the self-motivated, skilled citizen who can acquire, analyze, and create information that will contribute to the health and welfare of society. Even before the advent of the Internet and the access to enormous petabytes of machine-readable text, U.S. corporations bemoaned their requirement to spend millions of dollars to teach high school graduates to read, write, and perform basic mathematics. American students place well down the list of proficient students among lesser industrialized nations. Thanks to decades of intermittent federal investments in biomedical research, the pool of factual data amenable to analysis, and which should become part of all new physicians’ operational skills is becoming so large as to be unmanageable.
This IDR Team will engage with the crisis in education and the lack of a strategy to devise tools for efficient learning and will involve the intersection of neuroscience, engineering, and medical research. Under this umbrella neuroscientists who study memory and learning, attention, and decision making, could work with engineers and educators to develop innovative curricula that would help our young students cultivate expertise in dealing with the information overload they will encounter in and after their schooling. This broad topic represents a massive opportunity to create what Branscomb, Holton, and Sonnert (2001) have termed “cutting edge
research in the service of public objectives,” and what is sometimes abbreviated as “Science for Society” or “Jeffersonian Science.”
One major test ground for the implementation of methods for efficient lifelong learning could focus on the medical student who must learn not only the relevant facts and their application to disease mechanisms, treatment, diagnosis and prevention, but also to assimilate into that body of working knowledge all the new facts that will emerge during their careers as practicing physicians. This is also the case confronting tomorrow’s clinical trainees—and the paraprofessionals who will be needed to support them; the ever more rapid medical discoveries that need to be translated into care and prevention, the lack of time to train in federally funded residency programs and additional constraints imposed on this training by maximum hour work weeks, and a national healthcare plan that will reduce the Medicare funding for post-graduate clinical training.
While the IBM-Watson device and proprietary differential diagnostic systems—costing hundreds of thousands of dollars—are beginning to enter some forms of managed health care, such computer-assisted judgments can scarcely be an acceptable form of medical practice. Therefore, the underlying problem remains of devising an educational system that will not only motivate students to become skilled in basic academics, in the technology of any occupational discipline, but also evolve into a citizen who contributes back to society. Can a formal education system include only academic basics for collecting knowledge, or should it also include understanding the value of that knowledge, the processing of knowledge, the emotional value of inspiration, creativity, risk, and resilience from failures?
Two developments based on the use of information technology to support instruction and discovery that show some promise are learning management systems (LMSs) and the developing Semantic Web. The use of learning management systems, both proprietary and open source, to support traditional face-to-face instruction has been in place and widely practiced for well over a decade, but there is decidedly little scientific assessment of effectiveness. For those caught on the analog side of the digital divide, persons located in places ill served by telecommunications, the cadre of essential computer and network support personnel, and the instructors adept and willing to exploit the possibilities of LMSs, the possibilities are limited.
For those in the middle-of-the-bell-curve of usage of information communications and technologies, there seems to be benefits to the use of LMSs, such as: more efficient administration of courses with more supple-
mental materials, collaborative document creation, online study sessions, and practice and exams with final grades transmitted directly to student information systems. On-campus users of LMSs seem to interact more with the Web-based course support than do commuter students, but both improve performance in a course supported by an LMS. Among the environmental elements not tested is that of the engagement of social networking behaviors (via the likes of Facebook, Twitter, blogging, and similar) on intellectual development. Seemingly important information appearing as a result of a Web search on Google or Bing or similar have not been studied with the possible exception of the Stanford experiment by Thrun et al., who broadcast a course on Artificial Intelligence to over 100,000 “students” anywhere on this Earth. These experiments, and others such as asynchronous audio or visual course LMSs, deserve critical analysis.
The Semantic Web, a theoretical proposition envisioned by Tim Berners-Lee, the “inventor” of the World Wide Web, is intended to supersede the present chaos of the Web by the creation of a massive collection of information objects on the Web that “understand” one another in a machine sense, to create a structured web of documents enabling much more efficient retrieval of relevant information objects in response to human queries. As the number of machine readable statements of relationships with associated, unchanging Web addresses for the related information objects expands dramatically, the likelihood of the improvement of discovery of numerous ideas, objects, and references in numerous formats and genres that are highly relevant increases, while the time and effort necessary to search and retrieve those will decline dramatically, and hot links to the initial investigative entry will be created. The potential for computer-assisted lifelong learning as well as computer-assisted research at the highest level is also increased, without regard for the flood of new data joining the swamp of older data on the Web. The ability of these new agents to increase our intellectual reach without the necessity of remembering any more than the essence of the most relevant documents and the taxonomy of terms in the combined essences of one’s interests will expand our ability to deal with the flood and the swamp. Humans’ responsibilities to remember will become more nuanced, but our abilities or duties to understand, analyze, evaluate, and then apply knowledge will increase. The creation of new knowledge and the discovery of new relationships among ideas and facts and systems will advance the state of our comprehension of our world from the most atomic or even subatomic frame to the cosmological. The contributions made possible by this quiet revolution will address matters of
human health, our environment, transportation systems, education, and all the other aspects of our lives susceptible to rational thought and discourse.
Key Questions
• Can human knowledge acquisition and creation be made more efficient or more efficacious with computer-assisted learning systems, and if so, at what price, in what time, and in which arenas of society?
• In which domains of learning could such devices improve learning efficiency and in which are such improvements less certain?
• Is medicine/health the most societally-important test ground in which to apply such a learning system, or would the end result be improved with a longer time frame by starting with another test ground such as infants/toddlers?
• What is the evidence that scientific understanding has become more comprehensive and facile since scholarly journals went digital? Do scientists read more or less? Do they have deeper knowledge of their areas of specialization? Has time spent on literature researching improved the speed or breadth of discovery?
• Is there evidence that the brain is changing as the attributes of the World Wide Web, including social networking, are accessed often by various age cohorts?
Reading
Berners-Lee T, Hendler J, and Lassila O. The Semantic Web. Scientific American 17 May 2001;284;34-43.
Bird S, Bradshaw D, Chan WK, Clark C, Mears A, Milton U, Nuttall C, Palin A, Petroff A, Scholten B, Smith E. Online learning. Financial Times (Special Report) 12 March 2012. Bloom B. Taxonomy of learning domains. 5 June 1999.
Branscomb L, Holton G, Sonnert G, Packard and Sloan Foundations. Science for society: Cutting edge basic research in the service of public objectiveness. A blueprint for intellectually bold and socially beneficial science policy, 2001.
Brown E. IBM Watson: Final Jeopardy! And the future of Watson. TED Presentation.
Council on Library and Information Resources. Linked data for libraries, museums, and archives: survey and workshop report October 2011.
Keller MA. Linked data: a way out of the information chaos and toward the semantic web. Educause Review July/August 2011;46(4).
Thrun S, et. al. MIT/Stanford artificial intelligence experiment.
Watson, D. Pedagogy before technology: Re-thinking the relationship between ICT and teaching. Education and Information Technologies 2001;6(4):251-266.
Because of the popularity of this topic, two groups explored this subject. Please be sure to review the other write-up, which immediately follows this one.
IDR TEAM MEMBERS—GROUP A
- Giorgio A. Ascoli, George Mason University
- Vinton G. Cerf, Google, Inc.
- Alan D.J. Cooke, University of Florida
- Carolyn Crist, University of Georgia
- Felice C. Frankel, Massachusetts Institute of Technology
- Matthew K. Henley, University of Washington
- Roy Pea, Stanford University
- Shriram Ramanathan, Harvard University
- Laura L. Symonds, Neuroscience
- Mercedes Talley, W.M. Keck Foundation
Carolyn Crist, NAKFI Science Writing Scholar University of Georgia
IDR Team 1A was asked to develop innovative curricula that will help students gain expertise in dealing with the information overload they will encounter during and after their schooling. By looking at the needs related to attention, multitasking, and executive control in various age groups, this group debated whether to explore how to help increasingly distracted children who are in school, how to harness technology to create more data and information about the education environment, or how to teach students to mine and analyze the already overwhelming amount of data within various disciplines. As part of this discussion, the group agreed on the value of learning to preserve attention, focus, and ignore distracting influences.
The world now has an attention economy, and the learner has increasingly fragmented attention. Though there is more information than ever, it doesn’t necessarily mean media consumers are overloaded by it, the team decided. What media users are running out of is time and attention, and the problem occurs when attention is distracted, or users simply have an inability to filter the information. E-mails, chat messages, and social media
leave no time for serious work. This is happening at the same time that the demands for standards of learning, higher literacy, and specialized skills are ramping up, especially in the face of changing technology and an interdisciplinary approach to tackling problems. As part of this, the team acknowledged the expanding gap between the limited physical world of traditional schooling and the parallel virtual universe in which different and superior learning can be experienced, experimented, and designed as a vision of personalized mobile learning.
Several Solutions to Study Self
The team developed a “quantified self for learning” that would allow a user to measure, monitor, and make informed choices about his or her media consumption, time management, and productivity.
Dubbed many names—Nagster, Weight Watchers for Attention, or Attention Diary—the team created an idea for a program to help students maintain a log of what they have learned, as well as ways to capture data about themselves to analyze when and how they learn best. The idea is to foster reflection via easy access to a log of where they spend time and what they achieve.
For example, did the student eat breakfast before school, what is her emotional state while studying a certain subject, or what is the educational environment (such as witnessing a fight in the hallway) that is directly affecting her learning state? As part of this, the group proposed attention management tools that can track and visualize a user’s attention allocation related to goals and targets, coach behavior to help intervene and provide attentional focus, and provide a social environment to collaborate and stay accountable with other users in a defined community. A key component would include easy and automatic logging to reduce distraction and multitasking from the tool itself.
The program would include learner goals (study for a test, learn a language, practice sports), categories of attentional expense (for each activity contributing to a goal), measures of attentional expenditures (automated and subjective), and achievement progress for goals as part of attention allocation. The interface designs for this idea are variable, but the group agreed it needs to incorporate agency and be learner-designed and generated. Certain levels for elementary school, high school, college, and senior adult users will allow for scalability, user scenarios, and applicability related to federal laws. The group expressed equity concerns when it comes to differing ages, cultures, socioeconomic statuses, and learning disabilities.
Several specific ideas include a Mobile Focus Dashboard that captures and synthesizes this data for users, which visualizes attentional activities in relation to goals. A Nagster/Navatar computer application could represent an “embodied agent” or accountability partner. Reminiscent of Pinocchio’s Jiminy Cricket, Sesame Street’s Oscar the Grouch, or Star Wars’ Yoda, it reminds users when and how long to spend on a task, as pre-determined by the user. In addition, the team explored the idea of attention credits as a virtual economy that gives points for time and attention allocation that can be used throughout the week and help users to become aware of the limited inputs and outputs related to time. Finally, an immersive game could help users, especially young learners, to discover the effects of media multitasking as an avatar in a fast-moving world that must determine how to spend time and attention.
Overall, the aim is to help learners prepare for self-regulated inquiry, sense-making, learning design, collaboration, and self-reflection on attention, productivity, intelligence, and improvement. The idea is to create lifelong learners through a digital learning model that allows for agency and power.
Meet Marina, a Media User
As part of the design process, the team split into groups to sketch four “day-in-the-life” scenarios to determine the efficacy of their ideas, especially the Mobile Focus Dashboard. The groups addressed how elementary school, high school, college, and senior learners would use the system, and they discovered requirements for designs and issues for research to be effective at the individual, family, and institutional levels. To truly determine if the dashboard could be implemented, the group wished for more time to try rapid prototyping and piloting through iterative participatory design.
For example, as part of the high school user group, Marina is a 16-year-old junior in high school whose family immigrated from Guatemala when she was 5. Though her mother is not yet fluent in English, she is eager for Marina to develop knowledge and skills for a science and mathematics career. Marina’s four brothers and sisters and grandmother live with her in San Jose, California. As part of her media use, Marina is engaged with English movies and music, as well as Spanish telenovelas and music. She has frequent video chats with her family in Guatemala and constantly updates her friends on Facebook. She realizes that doing well in school is the key
to helping her family and feels a responsibility to do well, but she’s caught between keeping in touch with her family and friends and earning the scholarship that could take her to college. She may not complete homework for various reasons—chatting with family and friends online at night, caring for younger brothers and sisters, and participating in afterschool clubs that will enhance her resumé. The theme is that she has multiple cultural identities with multiple implications for media use and multitasking. As part of this learning system, she needs the ability to set goals but also allow time for reflection, exercise, and time for family and friends. By investigating specific details about Marina, the group acknowledged the importance of looking at both the quantity and quality of attention, as well as balance in a learner’s life.
Additional Questions for the Future
Much research is still needed regarding the concepts related to traditional and virtual education and how to manage the information learned during formal and lifelong schooling. The group developed the following questions:
Multitasking
• How are high multitaskers and low multitaskers defined, and how do they develop?
• What prevents a low multitasker/media consumer from becoming a high one?
• What do we value in relation to multitasking and media use, and do we need to help people move between categories?
• What types of multitasking scenarios are effective and ineffective?
• What measures are we using in the lab to study these multitasking scenarios, and are they truly relevant to the real world?
Attention and motivation
• How does this relate to memory and attention, and are we creating generations with attention disorders? How is this affecting work environments?
• How might social sharing and gaming elements, such as competition and prizes, help learners to manage attention?
• What are the intrinsic/extrinsic motivation variations, and how could this be incorporated with attention credits, or points awarded for time spent on a task?
• Are particular forms of distraction more disruptive than others, and how does this vary by learning domain and task?
• What is the impact of age, ethnicity, and other individual differences on distractibility?
• How can the user learn to manage his or her attention when not using the focusing-feedback system?
Data and media use
• How can researchers mine the data that is gathered from all of these sources and teach college students to harness the deluge of data to annotate new information and make discoveries?
• What are the social implications of high media use, short attention spans, and reliance on technology?
• What are the privacy and security concerns related to personalized mobile learning and collecting data?
IDR TEAM MEMBERS—GROUP B
• John-Paul Clarke, Georgia Institute of Technology
• Scott T. Grafton, University of California, Santa Barbara
• Shonali Laha, Florida International University
• Julie Linsey, Texas A&M University/Georgia Tech
• Wei Lu, University of Michigan
• Dejan Markovic, University of California, Los Angeles
• Jun Wang, Syracuse University
• Debra L. Weiner, Children’s Hospital Boston
• Michelle Yeoman, Texas A&M University
Michelle Yeoman, NAKFI Science Writing Scholar Texas A&M University
IDR Team 1B was asked to develop innovative curricula that will help students acquire expertise in dealing with the digital information overload
they will encounter during and after their schooling. As technology changes the world, the skills needed for success evolve. Students need to be proficient at analyzing, evaluating, and synthesizing large amounts of varying information. Adults need to be life-long learners with the ability to navigate through overwhelming amounts of information in order to succeed both at work and at home. Unfortunately, the fractured nature of the information overload encourages superficiality and generalizations, and discourages deeper analysis and synthesis.
Clearly, digital technology affects how students and adults learn—just what those affects are is not entirely clear. Also unclear is how this technology will continue to change society. What will be the educational needs of students in 2030? 2050? What challenges will the students of today face at work and at home? Are there educational strategies that can better prepare students for the future?
At first, the team had trouble narrowing the scope of this topic. One member suggested that students lack discipline and that a return to more traditional and structured classroom approach is needed. Other members emphasized the value of educational games in motivating and engaging students. Another suggested that parents limit the use of technology for their children. However, the majority of the team felt that this approach is impractical and could not be widely implemented. One team member suggested the creation of a protected web space, targeted at middle school students, which would help them learn how to manage and evaluate information. After some discussion, the team decided that its first goal was to characterize the deficits in the current education system.
Education in the Digital Age
The education system does not prepare students to cope with the information overload they encounter in this complex and changing digital age. Students lack the fundamental knowledge and analytical skills to evaluate information for relevancy, accuracy, and applicability. At the same time, the amount of information on the Internet continues to increase. Our education system needs to adapt to this new reality and prepare students for the information overload they already encounter.
The team then discussed what it considers the characteristics of superior education: fostering fundamental skills, encouraging small student groups, making use of educational games, and personalizing learning experiences. With fundamental skills, students can learn to manage and
filter the vast amounts of information available to them. Breaking students into smaller groups encourages interaction and fosters social development. Educational games can illustrate concepts and allow students to apply their knowledge. Catering to individual learning styles can also make learning fun, engaging, and efficient. The team agreed that these superior education methods should be incorporated into its curricula proposal.
While the team challenge was to assess the negative impacts of technology, team members also discussed the benefits. Digital technology, such as interactive learning games, can be a powerful educational tool that can improve learning, both in the classroom and at home. In order to maximize the potential benefits of the digital age on education, the negative impacts of information overload must first be resolved. One team member suggested that students and adults need a Hitchhiker’s guide to the Internet, which will help them navigate through the confusing and vast digital world.
The education crisis is a vast and complicated subject. The team decided that attempting to change classroom curricula for K-12 education is beyond the scope of the challenge. As a consequence, the team decided to focus on solutions to the information overload that could be implemented outside of classrooms or at the college levels.
The team discussed two basic approaches to information overload: 1) design an adaptive interface that filters the overwhelming amount of information for the user, and 2) teach students and adults fundamental skills and knowledge which enables them to filter information themselves. With this in mind, the team decided that an interactive, adaptive, and modular online system has the potential to do both—help users filter the information overload, and teach users how to analyze and evaluate this information themselves. IDR Team 1B proposed a model for this system called the Life-Long Learning Locker (L4).
Life-Long Learning Locker (L4)
The L4 would be an adaptive learning management system. The purpose of this system would be to select educational content based on individual learning styles and observed learning behaviors. Unlike other learning systems with a similar goal, this system will be customizable and adaptive—becoming uniquely personalized and tailored for the user. This learning system will be the optimal teaching interface, by adapting to the user’s individual learning style, interests, and current skill level in that subject.
As the optimal learning interface, the L4 will do the following:
Educate. The system will incorporate interactive tutorials, prepared educational content such as videos, and games to educate both students and adults.
Manage. L4 will have a customizable organizational structure with icons of shelves, bins, and folders in which to manage the vast amounts of information. Users can keep past content that they find useful. An example would be a pre-medical student keeping notes from an introductory anatomy class. Ideally, the system will be used from elementary school through adulthood, so that users have access to materials throughout their lives.
Filter. The system will incorporate a powerful search engine that helps users find and filter information available on the Internet. The search engine will uniquely tailor results to suit the user’s educational level, preferences, and needs. The filter would rely on ontologies (maps of related information) of semantic annotated Web pages (Web pages tagged with meaningful, identifying information).
Adapt. Search engine results and the sequence of educational content will be based on the user learning profile. The user profile will be characterized by user-supplied responses, observed characteristics of the user, and in response to observations in the community.
Potential problems with the system include privacy issues. Some people may be concerned because an outside entity is storing vast amounts of their personal information. However, current research suggests that the idea of privacy may become antiquated—some users in the Facebook generation have grown up without privacy and may not value it. Thus, privacy may be an issue for some people, but is unlikely to be an impediment to the application and use of this system.
Another potential problem with this system is motivation. However, multiple methods can be used to motivate the user. Games and interactive tutorials can make learning more enriching, engaging, and fun, which will encourage self-directed learning and increase motivation. A points system, among friends or with classmates, can also spur competition and act as a motivator. In addition, the L4 can be implemented in traditional and distance education classrooms, and a teacher can provide motivation for using this system by assigning research topics or educational content. Some team members questioned whether adults would be motivated to use the L4. However, other members suggested that adults who currently struggle with information overload may be motivated to use this system, provided
that it is helpful and practical. Additionally, students who have used the L4 during their education may be likely to continue using the system through adulthood.
Conclusions
The digital landscape simultaneously unites people from around the world, and distances individuals who are sitting across each other at dinner. As with any new tool, there are benefits and trade-offs for society and the individual. People are assaulted with information that comes in multiple forms and with varying degrees of accuracy and value. Attention spans are becoming shorter as people become addicted to the constant stream of incoming, fragmented data. Multitasking has developed as a way to cope with the information overload but is appropriate only for superficial tasks. As a result, people are becoming increasingly unable to synthesize the vast amount of information that is now readily available.
The team’s goal was to design a proposal that integrates available and emerging digital technologies to help students and adults manage the information overload that plagues this modern digital age. The team proposed the development of an adaptive, interactive, modular learning system that both educates and adapts to users. Ultimately, the system would help people manage and evaluate the overwhelming amount of available information by adapting itself to the user’s unique learning preference, then acting as a guide through the changing digital landscape. Were this hypothetical interface to be developed, users could not only manage the overwhelming information overload, but can even excel at its management.
