Running head

Dale Crowe

University of Phoenix

School of Advanced Studies

dalecrowe@email.phoenix.edu

Cell: 541-971-3259

1109 Kamerin Springs Dr., Talent, OR 97540
Martin LaPierre

University of Phoenix

School of Advanced Studies

mlapierre@email.phoenix.edu

Cell: 909-210-2334

Address: 40998 Promenade Chardonnay, Temecula CA 92591

University of Phoenix

School of Advanced Studies

mansureh.kebritchi@phoenix.edu

Cell: 407-496-1224

Address: 3935 Flowering Stream Way, Oviedo, FL 32766

Abstract

Introduction

Various advances in applications of augmented intelligence in distance learning have developed, but resistance persists among educators to adopt augmented intelligence applications. This resistance is partly due to confusion between the concept of augmented intelligence and artificial intelligence. John McCarthy was credited with devising the term artificial intelligence in 1956, and the philosophical conflict between these two concepts has continued for more than half a century (Lavenda, 2016). Augmented intelligence refers to increasing the capability of human beings to solve complex problems and gain better and faster comprehension of the situation (Engelbart, 1962). Augmented intelligence is related to the concept of an intelligence amplifier that Ashby (1956, 1960) first used.

Human intelligence is amplified by organizing intellectual capabilities into higher levels of synergistic structuring with the assistance of technology (Engelbart, 1962). In opposition, artificial intelligence refers to the capability of a machine to imitate intelligent human behavior (Merriam Webster, 1999). Artificial intelligence is in distinct contrast to augmented intelligence. Augmented intelligence seeks to empower and amplify human capability to solve complex problems. It is used as a supplemental tool to support the human being. The purpose of artificial intelligence is to reproduce human intelligence, function autonomously, and replace human intelligence with computer systems.

Artificial intelligence has been successful to simulate autonomous applications such as the IBM Deep Blue computer, which beat a chess grandmaster in 1997 (IBM 100, 2011). In 2011 Deep Blue was supplemented in some respect by the augmented intelligence of IBM’s Watson computer, but it is not possible at this juncture to fully replicate the entire human intelligence. There are limitations for simulating human intelligence. Although strength of artificial intelligence is as a mathematically modeling machine for processing information, human intelligence is superior for using intuition, interpreting context, and tolerating ambiguity in the processing of information. When artificial intelligence conceptually reaches such human abilities, an augmented intelligence approach would be useful for developing applications such as distance learning.

Artificial intelligence, with its potentially disruptive feature of replacing human beings, has overshadowed augmented intelligence and created trepidation among some experts including theoretical physicist Stephen Hawking, Microsoft Founder Bill Gates, and others (Gates & Hawking, 2015). Distance learning educators in particular have developed resistance for adopting artificial intelligence applications that are perceived to take away the educators’ jobs and act autonomously against educators’ plans. Shifting educators’ perspectives from artificial intelligence to augmented intelligence may reduce resistance and enhance adoption of applications involving augmented intelligence because augmented intelligence is grounded in human-to-computer interaction. The goal is harnessing computers’ best strengths while keeping human agents at the forefront, not replacing humans with computers. Such a view has been shared by a number of organizations and resulted in developing applications with an augmented intelligence approach.

Background

According to Kelly (2015) three eras in computing developed: the tabulating systems era (1900s-1940); programmable systems era (1950s-present); and cognitive computing era (2011-????). Cognitive computing era made its debut in 2011when IBM’s Watson augmented intelligence/KBS beat the greatest human champions of the game show Jeopardy. Since 2011, IBM developers have been working actively in the field of augmented/KBS intelligence.

Augmented intelligence/KBS may assist humans to better analyze, visualize, and comprehend the big data (Olshannikova, Ometov, Koucheryavy, & Olsson, 2015). In addition, Google Knowledge Vault, Wolfram Alpha, Microsoft Minecraft (AIX), Facebook (FAIR Team), and others are working to bring KBS to education. It may be time to take distance learning to a new level by incorporating what KBS has to offer in the way of administrative, tutoring, feedback, and research support.

Knowledge Based Systems and Big Data

A KBS is cognitive computing that understands natural language processing, adapts, learns, and generates and evaluates hypotheses. The system can help in the area of distance learning to assist instructors and students to make better decisions by penetrating the complexity of big data. The capability cannot be understated. According to IBM system engineers, 90% of the data in the world has been created in the last two years! (Rometty, 2016) and equates to 1.7 MB of data created for every person on the planet. In 2000 it was estimated that 500,000 people participated in online purchasing. In 2016 that number rose to an estimated 20-25 million purchases. According to projections by International Data Corporation (IDC, 2016) the digital world will reach 40 zettabytes (ZB) by 2020. To put this number in perspective, consider that 40 ZB is equal to 57 times the amount of all the grains of sand on all the beaches on earth. The quantity of data makes distance learning virtually unmanageable for keeping up with the growing volume and velocity of information available.

Unstructured or unorganized data, termed dark data, is a significant issue in cyber-space. According to IBM CEO Ginni Rometty (2016), 80% of the data in 2016 was dark and by 2020 this number will increase to 90%! From a librarian’s perspective Heidorn (2008) stated, “ . . . presently dark data [are] not carefully indexed and stored so it becomes nearly invisible to scientists and other potential users [distance learning students and faculty] and therefore is more likely to remain underutilized and eventually lost” (p. 280).

A KBS is one way to attempt the management of big and dark data by creating tools that generate meaningful data. For example, using the cloud-based IBM Watson (Watson) platform, all the known articles, texts, papers, presentations, and research studies on cancer have been stored in Watson (Kohn et al., 2014). Physicians and other health care professionals can have access to these materials in less than two seconds to acquire a potential diagnosis and recommendations for treatment. Taking health care as the model, it is entirely possible to port this technology to other areas including distance learning and education in general.

Cognitive computing systems, including augmented intelligence/KBS, primarily work by interacting and learning in a natural way with people to extend what both humans and computing can do on their own. KBS is not thought of as artificial intelligence per se, although it has many of the attributes; rather a KBS operates through augmented intelligence to help human experts make better decisions by navigating the issues surrounding big and dark data (Kelly, 2015). In the world of distance education managing big data can be accomplished by enhancing the cognitive process of distance learning professionals, faculty, and students to help improve one’s decision-making process in the moment. According to IBM’s Institute for Business Value (2015), there is already a belief among subject-matter experts such as instructional designers that current computer architectures and programming paradigms must advance cognitive computing to include natural language processing.

Natural Language Processing

Assimilating the vast amount of information academics and others publish is a daunting task for students and faculty, especially in the distance learning environment. Using sources such as online libraries, Google Scholar, and other web-based sources can be challenging. Natural Language Processing (NLP) uses natural language to mitigate such accessibility issues, but the form of NLP developed for Watson, for example, is limited to English at this point. According to Ferrucci, Levas, Sugato, Gondek, and Mueller (2012), NLP/Watson does not plot the inquiry to a database of questions and simply search for the answer. As a form of machine learning architecture, NLP/Watson performs by analyzing natural language content in both questions and knowledge sources. Further, NLP/Watson (a) assesses probable responses, (b) tallies evidence for those responses, (c) discovers and evaluates potential answers and (d) gathers and scores evidence for those answers in amorphous sources. Sources include natural language (NL) documents and systematized sources such as relational databases and knowledge bases. A definition of NL is, “A human written or spoken language as opposed to a computer language” (American Heritage, 2011).

Machine Learning and Application of Augmented Intelligence

Knowledge based systems use a hybrid form of machine learning. Machine learning can be considered an extension of artificial intelligence that allows distance learning application developers to automate analytical model building. This is accomplished by assembling algorithms that can learn from and make estimates based on data (Intel, 2016). One of the applications of augmented intelligence is the intelligent tutor system that refers to any computer system containing interactive applications with some intelligence that facilitates the teaching and learning process (Rodrigues, João, & Vaidya, 2010). Intelligent tutor systems encompass the major components of one-to-one interaction between the instructor and learners, customized instructions based on learners’ needs, and individual feedback for the learners. The systems have been developed within various organizations during the last half a century. A recent meta-analysis conducted by Kulik and Fletcher (2016) from 50 controlled evaluations of intelligent tutor systems indicated that use of the systems raised students’ performance well beyond the level achieved by students taught with conventional methods by human tutors or another form of computer tutoring. One of the recent successful intelligent tutor systems is Intelligent Tutoring for Lifelong Learning (I-TUTOR) that is a multi-agent-based intelligent system to support online teachers, trainers, instructional designers, and tutors. It is supported by the European Commission and can be used in open source learning environments. In collaboration with Pearson Learning, IBM’s Watson will be used as an intelligent tutor in online as well as traditional classrooms (IBM Pearson, 2016).

Distance Learning and Knowledge Based Systems

Data from the National Center for Education Statistics (NCES) indicated that in the fall of 2013 there were 5,522,194 students enrolled in distance-education courses at degree-granting postsecondary institutions (NCES 2014). By comparison, K-12 high school distance education enrollment was over 1.3 million from the period of 2009-2010 and constitutes an increase of over 1 million from 2004-2005, when enrollment was approximately 300,000. The 1.3 million distance learners’ represents 53% of all the high school students in the U.S. (NCES, 2012). According to a report published by the Babson Survey Research Group (2015), in conjunction with Online Learning Consortium, Pearson, WECT, StudyPortals, and Tyton Partners, found more than one in four college students take at least one distance learning course (total of 5,828,826) students, representing a year-to year increase of 217,275).

With this substantial influx of distance learners comes the challenge of disseminating quality curricula and associated support tools. According to He, Cernusca, & Abdous (2011), a majority of distance learning programs is unable to meet the emerging demand and requirements for both students and instructors. In addition, there are also challenges to cognitive computing. A key challenge for the advancement of cognitive computing or augmented intelligence will be the availability of skilled humans as subject matter experts to further the development of cognitive computing. Advancing cognitive computing capabilities and implementing cognitive systems require unique skill sets, such as those of machine learning experts and natural language processing scientists. These skills are currently in high demand and limited supply (Kelly 2015). Using a distance learning/blended learning platform is one way to train students to meet these job demands.

Knowledge Based Systems and Watson for Education

Knowledge-based systems development is at the cusp of emerging in education and distance learning. Building on Watson’s health care model, IBM system engineers are applying its cloud-based services to education. In late 2016 IBM released the mobile product IBM Watson Element for Education. Using Watson, the mobile application provides educators with insights on their students from a consolidated 360-degree view from a range of data sources (IBM Education, 2016). The application uses a variety of data sources, including demographic, academic, attendance, assessment, social and other data. Another benefit is that teachers can be alerted to important information about student progress to facilitate provision of immediate guidance if necessary.

In 2016, IBM released IBM Enlight For Educators, which consists of two components: analytics and a library. The first component, IBM Watson Education Analytics “. . . uses a single data representation of each student to provide the educator with a comprehensive profile” (IBM Education, 2016). One creates the profile by gathering data from numerous sources, including demographic characteristics, academic history, student interests, behavior, academic strengths/weaknesses, etc. The second component is the IBM Watson Education Library. Developers consolidated learning content to provide educators with a “simpler, rich source” of materials (IBM Education, 2016). With this component, cognitive computing or KBS can provide analysis that aligns with a curriculum and other resources including academic standards and customized instruction for the individual student (IBM Education, 2016).

Both IBM Watson Element for Education and IBM Watson Education Library can be a potential benefit for those teaching distance learning courses. One issue for many distance learning educators is time. Tools developed by IBM, and the others may help with the demands of time management. A second benefit using IBM Watson/KBS is managing and organization of courses. Distance learning involves synchronous and asynchronous virtual instruction that creates its own set of challenges when delivering, managing, and organizing distance learning.

Knowledge Based Systems and Microsoft®

Microsoft has entered the distance learning education arena with the online game Minecraft™. Popular with ages ranging from elementary school to adults, Minecraft™ is an example of where augmented intelligence and KBS are emerging. Microsoft Corporation® saw the benefit for future cognitive programs by purchasing Minecraft™ in 2014 for $2.5 billion dollars. In November 2016 Microsoft launched Minecraft: Education Edition. In the Minecraft: Education website introducing the product, the writers point out what the platform has to offer, including lesson plans, starter worlds (AI/Virtual) a place for educators to collaborate and a mentoring program (Minecraft, (2016). The Minecraft™ mentor program connects educators with others experienced in teaching with Minecraft™ to allow educators to explore ideas and lesson plans created by educators covering topics from storytelling and poetry to city planning, sustainable living and geometry.

Technology Behind Using IBM’s Watson for Development of Tools to Support Distance Learning

Although cognitive learning platforms by various developers (e.g. Microsoft, Google, Facebook, etc.) are emerging, the Watson expert system appears to be one of the best solutions to facilitate the creation of a myriad of curriculum and administrative tools for distance learning as well as ground-based applications. Is Watson an expert system? Some experts say that it is a form of an expert system but goes beyond. Watson architecture is complex. This complexity makes it challenging to determine in which category Watson fits, beyond IBM’s coined term of augmented intelligence. Murdock (2013) believed that in some ways it could be classified as an expert system.

Expert System

An expert system has a two-part structure consisting of an inference engine and a knowledge base. The inference engine operates analogously to human reasoning by using the facts presented to it by the knowledge base to infer the logical results of a proposition initiated by the user. The inference engine acts on the data by applying “if-then” type rules. Every if-then rule has multiple conditions and multiple actions from which to choose. Particular conditions must be true for Watson to initiate specific actions. One deduces relationships between the data presented and the actions Watson initiates by interpreting the first-order logic-based rules against the knowledge in the knowledge base (Ferrucci, Brown et al. 2010).

A knowledge base is a database that is structured into an ontologically consistent format and is analogous to an encyclopedia. Ontological consistency of the knowledge base allows Watson’s inference engine to make deductions that are valid and consistent within the particular domain of instruction that is required. The knowledge base contains two types of knowledge: expert and general. When a user queries an expert system like Watson, the inference engine applies rules to the information contained in the knowledge base and new information is created. After the Watson system verifies and validates the new knowledge, it is stored in the knowledge base with the same ontological constancy with which the original knowledge was stored. This is how expert systems like Watson learn and continue to grow to be increasingly more useful over time.

Constructing a Knowledge Based Systems for Distance Learning

Constructing a KBS for distance learning using Watson may be a matter of consequence. It may be that all the resources required for the construction of a KBS for distance learning are not already an inherent part of the systems that make up Watson. If that is the case, then there might be a need to build additional cognitive computing components with logical symbolic relationships expressed as a series of rules. That type of programming requires the use of a specialized language that is fashioned to use non-numeric or symbolic computing techniques known as Prolog.

The name Prolog is derived from the words Programming Logic. Prolog is based on formal logic and is considered to be a high-level programming language because it is machine independent and can be read by humans. Prolog was built to search a knowledge base, performing read and writes operations while preserving ontologically consistency. Prolog can list facts and rules, define and solve logical expressions and is also considered to be a declarative language. Prolog was used by IBM’s computer scientists, in part, to build the Watson system so it is among the best, and possibly the only, computer language choice to use in extending Watson’s cognitive computing capabilities (Michalik, 2011).

Extending the cognitive computing capabilities of Watson is a much different type of programming than building new applications using Watson’s Application Programming Interface (API). When you use Watson’s API you are building an application that draws upon established application libraries that have known inputs, outputs and behaviors using a procedural language. A procedural language is one that requires the programmer to construct a series of well-formed and precisely described increments or steps. It requires a defined and well-controlled vector or flow through the program. Our modern society is based upon this type of programming because it is useful in performing tasks repeatedly given certain inputs; the computerized optimization of an internal combustion engine for maximum performance is a relevant example of this concept. When extending the cognitive computing capabilities of Watson using prolog you do not write it as you would a procedural language, line by line. Prolog is a declarative language that has to be written by describing a situation. The prolog compiler will tell you if the prolog sentence that was written is true or not and it will also tell you what the variables are in that sentence without the programmer having to declare them. This is a difficult style for many programmers to use let alone master, but it must be utilized because it is the language that provides Watson with its most valued capabilities.

The difficulty in using prolog mostly resides in how it is used to represent knowledge about the world. Representing knowledge about subjects being taught via distance learning requires an ontological understanding of the subject being taught and the data structures that prolog requires in presenting that knowledge. With any given subject, the programmer must understand the properties, types and interplay of the entities that exist for the domain of study. This understanding when instantiated in prolog code will help prolog construct the variables needed for the symbolic computations that need to be carried out for Watson to master the domain of study or instruction.

Creating a useful ontology and successfully extending the cognitive computing capabilities of Watson is not divorced from procedural programming. Functional programming takes procedural programs and contains them in functions, as the name implies. Functions can be used to build data structures that are used to control the flow, input and output of data. Procedures contained in functions are required to ensure that ontologies are performed in predictable ways that can be used to create distance learning applications.

When programmers first started programming with knowledge bases in ontologically consistent formats, it became apparent that scaling would be difficult, if not impossible, unless formats were modular with the ability to communicate. Programmers used ideas derived from procedural and functional programming to encapsulate small and highly specialized knowledge bases into small domain-specific units with the ability to communicate between the units. Such functionality enabled programmers to use ontologically consistent knowledge bases as building blocks of object-oriented code, thus providing unprecedented scalability.

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According to Füssl, Streitferdt, & Triebel (2015), Watson and KBS makes use of such techniques for scalability that programmers can extend through “DeepQA” (p. 94). Deep QA has an immense corresponding, component-based pipeline architecture that uses an extended set of structured and unstructured content sources. The architecture supports a range of “pluggable search and scoring components” for incorporation of several analytic techniques to apply to distance learning (p. 95).

Use of Knowledge Based Systems in Distance Learning

Feasibility and development of KBS for use in distance learning is directly affected by the tools available for use in extending the cognitive computing capabilities of Watson. To a large extent the current tools are primitive for bringing together all the required programming paradigms in developing a KBS for distance learning. Despite the limitations, Watson has come a long way since winning the Jeopardy contest in 2011 with a cluster of 90 stand-alone servers. In 2017 it is possible for a development team, including instructional designers, to purchase time on the IBM Watson Developer Cloud without the added expense of owning servers (IBM Developer, 2017). Creating the necessary tools required for the development of KBS applications for distance learning is the first step. Educating programmers, instructional designers, and subject matter experts to use the tools needed to develop a KBS for distance learning is a necessary requirement. With augmented intelligence application development, the human factor is present and needed.

Tools needed to develop a KBS for distance learning will not only include traditional code-based tools. It is possible that specialized hardware also could be created. It is conceivable that KBS applications for distance learning could drive specialized hardware that will allow more rapid communication between devices on the Internet of Things (Cadavid, Ibarra, & Salcedo, 2014). Specialized hardware for KBS applications in distance learning would be required to speed up communications between the ontologies contained in the functions that IBM’s Watson uses in its cognitive computing paradigm.

Precedence in Web Programming

Creating tools for the development of a KBS for distance learning and teaching instructional designers and programmers to use the tools has precedence in web programming. Web programming is the term associated with creating internet-based websites and internet-based applications and web programming has changed the way we live and work. Creation of the first Internet web browser sparked a revolution in information-based programming changing the way we shop, diagnoses illness and communicate with friends and family. Whole industries have been created because of the capabilities of the web programming paradigm. According to Hanus & Koschnickek (2014) web programming developed relatively quickly, within twenty years, to become a driver of industry, health care, recreation and social interaction.

Knowledge Based Systems Beyond Web Programming

Development of the tools required to construct a KBS for distance learning may have an equal or greater effect on society, as did the development of web programming. The development of Watson may be one of the biggest events in computing history along with the development of the Internet and Internet browser. The use of a KBS for distance learning applications will not only enhance education in general but may allow individuals the opportunity to learn and keep current in almost any type of profession with just an internet connection. It will also enable individuals to obtain knowledge and understanding far more quickly and at a younger age than has been possible in the past.


Feasibility Study: Knowledge Based Systems Using Watson for Distance Learning Applications

Twenty instructional technology and computer science subject matter experts were individually interviewed as part of a study to investigate whether developing KBS distance learning applications was feasible. Questions presented to the participants were:

1. 
   Can the knowledge-based system perform syntactic and semantic recognition in

   support of the creation of a KBS for distance learning?

   
   

2. 
   Do the unique challenges facing instructional designers and information technology developers in producing knowledge-based programs/applications make a KBS feasible to produce?
   

A second goal was to explore whether a conceptual framework exists in current academic programs for incorporation into a development plan/model that can be migrated to a future KBS scholarly writing solution. Given the current and emerging state of technology, 18 of 20 participants were confident that KBS could be migrated from health care to develop and support curriculum and administrative distance learning applications.

With the information the participants provided, a prototype writing tutor application was developed with the help of a group of subject matter experts consisting of an instructional designer/distance learning faculty, computer programmer, and expert in the areas of grammar, style, and mechanics. Researchers, working with IBM, are using the Watson Developer API cloud-based system for this venture. It is hoped to have a beta version available in late 2017.

Conclusion

A continued focus on harnessing augmented intelligence/KBS to support educators in distance learning is expected. For instance, IBM has plans to continue learning about human intelligence and how people learn to be able to assist educators improve teaching and learning processes using augmented intelligence applications. For IBM developers, a goal is to “fundamentally bring the element of discovery, surprise and exploration back into the classroom, and in the process, deeply engage the learner.” (Smith, 2014, p.1).

With the emerge of many augmented intelligence systems, instructional designers play key roles in adopting augmented intelligence applications to identify the appropriate systems, inform educators about the potential of augmented intelligence systems, and incorporate them into the educational system and learning modules. Instructional designers should have multiple roles as researcher, innovator, and informer. It is, perhaps, instructional designers’ responsibility to shift the perspectives of educators from the philosophical concept of artificial intelligence to augmented intelligence to reduce educators’ fear and hesitation in adopting augmented intelligence applications. In addition, instructional designers should inform educators about the limitations and strengths of augmented intelligence applications and their relation to human intelligence. Knowledge based augmented intelligence can enhance the distance teaching and learning process.

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Bios

Dale Crowe – is an associate faculty member at the University of Phoenix School of Advanced Studies and a Research Fellow at the Center for Educational and Instructional Technology. His PhD is in Instructional and Information Technology and also holds a Juris Doctor degree. Dr. Crowe has been a technology consultant/IT project manager for K-12 schools, higher education, corporations, health care, and museums for over 25 years. He was on the faculty, and a research associate for two high research activity universities. He has presented at several domestic and international conferences and is a peer reviewer of research textbooks for Sage Publications.

Martin La Pierre – is an Independent Computing infrastructure consultant at Ferentina, Inc., Doctoral Candidate, University of Phoenix, Col. USMC (Ret). He also holds a Master’s Degree in Artificial Intelligence and Computer Science from De Paul University , a Masters of Business Administration from National University and a Baccalaureate Degree in History from the University of Illinois Chicago.

Mansureh Kebritchi is the founder and chair of the Center for Educational and Instructional Technology Research, School of Advanced Studies, University of Phoenix, where she supervises more than 200 faculty members to conduct research in the field of educational technology. She holds a Ph.D. in Education, Instructional Technology and has a wealth of experiences in managing faculty members to conduct research, teaching, mentoring doctoral students to complete their dissertations, conducting research, and publishing and presenting the results in the field of education. She is interested in studying innovative ways to improve quality of teaching and learning in K-12, higher education, and corporate settings in online and face-to-face formats. Some of Dr. Kebritchi’s recent publications address topics such as issues and challenges for teaching successful online courses, facilitating styles of online instructors in course management systems, pedagogical foundations of the educational computer games, factors affecting the adoption of the computer games in school settings, and effects of mathematics games on mathematics achievements and motivations of learners. She has been continuously publishing and presenting her research results in international journals and conferences.

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