Efficient Learning Maps (eLms): a Cloud Mapping ... - LearnTechLib

Efficient Learning Maps (eLms): a Cloud Mapping Engine to Improve the Thinking and Study Skills of High School and College Students in STEM Disciplines Michael Tang, Department of Civil Engineering Yujie Xu, Department of Computer Science Yu Xiao, Department of Computer Science Ashley Farrell, Department of Mathematics Karen Knaus, Department of Chemistry University of Colorado Denver

Abstract Efficient Learning Maps (eLms) is an automated mapping engine that is part of a software application called eThinker©, designed to improve the thinking and study skills of senior high school and college level students in the science, technology, engineering and mathematics (STEM) disciplines. This paper gives an overview of the theories behind the a eLms mapping engine and a technical description of the engine which is a plug-in to Microsoft Word’s Table and Shape functions to teach users how to generate a structured set of Visual Learning Maps (vLms). These visual learning maps are isomorphic to seven formal logical expressions and are designed to help students reduce complex reading material to simple relational units for better comprehension and more efficient learning. This paper is based partially on two previously published theoretical papers, one published by the AACE (Tang, Hyerle, & Tran, 2012) and another by the International Journal of Technology, Knowledge and Society (Tang, Toan, Kim, Hund & Knaus, 2012). Introduction According to the International 2009 PISA Test, the United States ranks 22nd and 23rd respectively in thinking, math and science skills relative to other countries and regions.

Thinking Skills 1. Hong Kong 107 2. Taiwan 106 3. Korea 105 4. Japan 104 5. Singapore 103 . . 20. France 98 21. Mongolia 98 22. USA 98

Math Skills 1. Shanghai 600 2. Singapore 562 3. Hong Kong 555 4. South Korea 546 5. Taiwan 543 . . 21. Hungary 490 22. Luxembourg 489 23. USA 487

Science Skills 1. Shanghai 575 2. Finland 554 3. Hong Kong 549 4. Singapore 562 5. Japan 539 . . 22. Ireland 508 22. Poland 508 23. USA 502

Fig 1: Relative thinking, math, and science skills, Source: (PISA, 2009; Lynn, 2002). A score of 100 was the average score in the area of thinking skills.

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It also is well known that American scores on standardized tests such as the Standard Achievement Test (SAT), widely used for college admissions, have been declining since the 1960’s. According to the Los Angeles Times, “SAT scores for the class of 2011 decline in every aspect,” with high school senior reading scores being the lowest ever recorded. Moreover, 75% of the class of 2011 did not meet college readiness benchmarks in STEM disciplines (Rivera, 2011). Clearly, an urgent need exists for educational interventions that can be used to improve the academic skills of high school and college students in these disciplines. The purpose of this conference paper is to describe an automated mapping engine that can be used to generate a set of logical mind maps to improve the thinking and study skills of STEM students as an effective solution to STEM education challenges facing American students in the 21st century. The mapping engine, Efficient Learning Maps (eLMs) is part of a software application called the eThinker, which also has other interactive learning engines to teach complementary aspects of thinking and study skills. The eThinker is one of several other software applications in a suite of such applications, called the eTutor. These other software applications include: eSpeaker, A software application to help students increase digital reading speed; eReader, an online text to speech software application to improve digital reading comprehension and analysis; and eReport, an artificial intelligence engine that automatically organizes input textual information into: 1) a keyword concordance, 2) visual learning maps, and 3) a vector analysis of keywords in context, a kind of automated highlighting of main ideas for study and retention. eSpeaker eReader eTutor

eReport

eExercises

eThinker

eVocab eLearning Maps (vLms)

Fig 2: The subcomponent applications within the eTutor and eThinker.

Visual Learning Maps (vLms) Visual Learning Maps are a unique and specific subset of mind maps. In general, “mind maps” are diagrams used to generate, visualize, structure, and classify ideas, to aid organization of information for study, solving problems, and writing. The elements of a given mind map are arranged intuitively according to criteria such as importance and connections between concepts, and are classified into branches, areas, or groups, to represent semantic or other relationships found in the subject matter being analyzed (Budda, 2004). The essential difference between mind maps and the specific subset of visual learning maps described in this paper is that the Visual Learning Maps are not constructed intuitively, but rather rigorously constructed based on cognitive linguistics and formal logic. Tang et al. (2012) detailed the cognitive and linguistic science principles behind the maps and Marshall et al. (2012) presented some preliminary data

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from a study in their paper indicating that the use of Visual Learning Maps led to greater netlearning gains in content knowledge among lower ability students. Tang et al. (2012) found that visual learning maps can be reduced to the following root equations: x εΑ;

A

(see fig. 3). The Visual Learning maps also can be distinguished from mind maps in that the main elements of the vLms are arranged in very specific spatial patterns and the different arrangements of the map elements denote different meanings or logical relations. The vLms are spatial as well as visual in that where and how elements are located in a map have meaning as to the kind of logical relations being represented. For example, a vertical placement of elements in a tree form represent genus-species relationship while a horizontal placement of terms would represent a structural relationship as in figure 3. A

Classification x A

Operation

Structure

Similitude A

A

Parts

Properties

Linear

Recursive Analogy

Metaphor

Fig 3: Classification chart of vLms with corresponding logic symbols. The five kinds of root maps are shaded (in (shaded)

Each of these maps asks and answers a specific first-order logic question concerning relationships which in natural language is represented by logical thought expressions such as, “is a kind of_______?”, “is a part of________?, “is a property of_______?” and the like. These maps are four in number and each element is linked to other maps by the hyperlink to build more complex maps. Like mind maps but in much for structured way, the vLms are used to help visualize, and classify ideas to improve organizing information, solving problems and logical thinking. Unlike mind maps, however, vLms maps are constructed primarily for exegesis or the analysis of hard text and digital textual materials with an emphasis of reading materials in the STEM disciplines. All maps can be reduced to the basic proposition: x A. The five root maps are as follows:

Classification Map (x

A) =

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The classification maps is the root map of the entire system and is often represented as a tree as in figure 3. When a student diagrams a map in a tree form, using the eLmaps application, he or she is answering the questions, “What is this a kind of what are the kinds of this?” Although the tree can be used to diagram many kinds of relations, the classification map is limited to the genus species or parent-child relation, and can be generated deductively (top-down) or inductive (bottom-up).

Structural Maps

The Structural Maps are of two kinds: the Parts Map and the Properties Map and as with the classification maps can be constructed inductively or deductively. The Parts Map is used to analyze and describe parts of things. Parts can easily be distinguished from properties in textual analyses in that the elements in these diagrams are usually nouns or proper nouns that point to material things, including people. A property map analyses or describes the properties of things and can be distinguished from a parts map in that properties are qualities that belong to things rather material components. An example of a Parts Map is found in figure 2, which detailed the main parts of the eTutor software suit and the three computer engines in the eThinker application, including the Efficient Learning Maps (eLms). What follows in the next figure (4) is a Properties Map that show what a hypothetical person by the name of Jane does, Bikes

Jane ( Runs Paints

.

Figure 4: property map of what Jane does (i.e. functions). Note in this case the word elements are verbs

If we add some of Jane’s personal qualities, that is, “she is a tall, smart and a brunet,” to what she does, the result is the following map in figure 5. Smart

Bikes

Tall Runs

Jane Plays violin

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Brunette Figure 5: property map of Jane, telling us that not only does she bike, swim, and hike, but she’s also smart, tall and a brunette. Note that the elements, “smart, tall and brunette” are adjectives.

Jane’s properties, qualities or activities should be distinguished from her physical aspects, in which case we use the parts maps for analysis as in figure 6. Note that Parts Maps usually consist of nouns while Property Maps are distinguished by the use of adjectives, adverbs, verbs and gerunds (verbs that act as nouns). Both maps are a kind of Structural Map as per figure 3.

Eyes Head Jane

Torso

Hair Arms

Limbs

Long Brown

Legs

Fig 6: a parts maps showing Jane’s material parts, which should be distinguished from her properties, attributes, functions, or qualities. Note that word elements in Parts Maps are usually nouns representing material things. In addition, we mixed another root map into basically a parts, part, a quality map describing Jane’s hair (adjectives). Long and brown, with a red connector indicates properties (adjectives) instead of parts(nouns).

Operation Maps. (if A

B)

Operation maps are different from the foregoing maps in that they are used to analyze change and processes. Although the list, like a tree, can be used to organize information describing different logical relationships, the list in our schema is used exclusively to illustrate change, especially sequence, although the elements within the major stages of a process need not adhere to this rule strictly. Figure 7 illustrates two kinds of operational, stage or process maps.

Morning >Get up >School Afternoon >Lunch >Work Evening >Dinner >Study >Sleep

Thunder Lighting Clouds

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Rain

Fig 7: operation maps, where use of arrow indicates cause and effect or an if-then statement

Similitude (a:b :: c:d) or more simply: A root similitude means, A “is like” something else. Analogies and metaphors are more complex forms of a similitude. Rigorous examples of analogies are those found in the Miller Analogies which often appear in standardized tests, especially for graduate school admissions as analogies are purported to measure higher level thinking. Miller Analogies have become a favorite testing item to measure aptitude in computer science. The analogy is basically a more complex form of a similitude, or correspondence as in A B, A corresponds to B or A is similar to B. Analogy Maps are two dimensional presentations of the relationship, A is to B as C is to D. For example, in figure 8, painting is similar to music in that both are art forms and in the arts, Mozart is to musich as Monet is to painting: Map a

Map b

Mozart Painting

Monet

Arts

Music Music

Painting

Fig 8: An analogy map, which is similar to a properties map but has no connecting lines and is arranged in a tetrad pattern. The root map for the analogy map is the similitude = .

Syntax Maps The foregoing five maps are root maps or basic elements of what could be considered a visual language. The next two so-called maps, are syntax maps in that they are structural units or the syntax or grammar that connects the five root elements to form more complex wholes or systems. The Frame The frame is basically a table to help the user organize information easier. More importantly, it functions as a map or structure that provides the background or other information to help users understand or interpret the elements contained in root maps. The frame provides the context or matrix to give clearer meaning to any of the six maps that may be ambiguous. For example, in figure 9 “ card games” is the frame for drawing the “ bridge” map, where the term “ bridge,” may be confused with an engineering structure where the main purposes would be completely different than those of the card game. An analysis of an engineering bridge would be better served initially with a Parts Maps consisting of beams, trusses and arches. Card

Trick taking

Bidding

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Games

Bridge

Scoring

Trumping

Fig. 9: where the meaning of “ bridge” becomes much clearer, when we label the frame “ card games.”

The Link What connects one word or image to another on the Web is the hyperlink, and this same link when constructing more complex vLms connects one map to another by similitude or association. After reducing complex text to the simple maps, we can connect these simpler units into the complex via the link. This is where the creativity and magic happens. In figure 10 note that our complex map consists of different logical maps mixed together as well as linked together. For example, Jane’ s quality of being athletic is broken down into the kinds of athletic sports she excels in, which is track and tennis. Her traits of being a polymath and an ectomorph, on the other hand, are connected to other maps via the link.

Tall

Thin

Body

Athletic Track

Tennis

Polymath

Young Jane

Abilities

Ectomorph

Math Music

Arts

Literature Art

Frame: Jane as polymath in college Fig 10: linked maps: where several root maps are linked. Note for nouns we use a tree, and for adjectives we use a properties map.

The eLms’ s GUI connects to Microsoft’ s SmartArt, Table and Shapes functions and reduces the number of steps needed to draw the necessary shapes to construct the Visual Learning Maps. The Smart Art plug-in can be used to make PowerPoint presentations for recitations and other kinds of multi-media presentations. The mapping engine application, therefore, is basically a

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drawing program that sketches out the Visual Learning Maps we have just described and discussed. eLms is is written in HTML5, PHP and JavaScript to teach students how to use the Visual

Learning Maps to organize information into concepts and systems analysis. The five root maps and two syntax maps represent fundamental logical operations in graphic form and the engine will generate the vLms, augmented with accompanying text manuals and built in audio/visual add-ons as learning aids to teach the maps to both teachers and students as users. eLms stores the visual learning maps as

Microsoft Word documents and is connected to a SmartPen to allow for more free style map construction. The SmartPen includes a microphone, built–in speaker that plays back recorded audio; an infrared camera that captures written notes and drawings; an OLED display and a USB connection that transfers notes and audio to the computer and recharges the pen. (http://www.livescribe.com/en-us/smartpen/pulse/). Maps drawn using the SmartPen can also be saved as Word files.

The sparse nature of the visuals generated by the eLms program are based on the computer design theory that “ less is more,” and cognitive load theory that visual diagrams and computer learning systems are less effective when they overload the mind with too many concepts, stimuli and choices. Conclusion The developers of the software application called the eThinker with a mapping engine that generates Visual Learning Maps cautiously suggest that we have an education technology that can have a significantly positive influence on improving student academic performance in the STEM disciplines once it has been thoroughly tested and widely disseminated. This new technology is based on the computer design principle of “ less is more” and extensive cognitive science, linguistic and assessment research in mind maps to develop the Visual Learning Maps which the eLms machine constructs. This design principle and our extensive research have resulted in a cloud computing technology which preliminary tests show may make a difference in helping high school and college STEM students think and study better because of the maps logical and visual structure encourages rigorous and at the same time creative systems thinking. Such education technologies are urgently needed to help our students perform better in thinking, science and math skills to regain our prominence in these areas of study worldwide. References Cited 1. Budda, J. W. (2004). Mind maps as classroom exercises. The Journal of Economic Education. New York: Routledge. 35 (1). 35-46. Hyerle, D. (2009). Visual Tools for Transforming Information into Knowledge. 2nd Ed. Thousand Oaks, CA: Corwin Press. 3. Lynn, R. and Vanhanen, T. (2002). IQ and the Wealth of Nations. Westport, CT: Praeger 4. Marshall, W., Tang, M. & Durham, S. (2012). Integration of science, technology, and society (STS) courses into the engineering curriculum. Journal of Engineering Education. Washington, D.C: ASEE (In Press). 5. PISA (2009). Results: OCED Programme for International Student Assessment., www.oecd.org/edu/pisa/2009

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6. Rivera, C. (2011) “ SAT scores for class of 2011 decline in every aspect,” Education Section. Los Angeles Times (September 1). 7. Tang, M., Hyerle, D. H., and Tran, T, (2012). A mathematical analysis of semantic maps. International Journal on E-Learning. Chesapeake, VA: AACE. 11(1), 95-104. 8. Tang, M., Tran, T., Kim, H. J., Hund, A. & Knaus, K. (2012). Efficient learning maps (eLmaps): An educational software application based on principles of cognitive science. International Journal of Technology, Knowledge and Society. Champaign, IL: Common Ground Publishing: (in press). 9. Upton, A. (1961). Design for Thinking: a First Book in Semantics. Palo Alto, California: Stanford University Press

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