Cost-effective: Previously produced materials can be repurposed and reused by any number of users. Rather than producing new basic content with limited production funding, IMS will provide access and use of existing materials to meet a variety of students’ learning styles. When existing materials cannot be used or are not appropriate for some reason, production funding can be used to produce the new content objects. The new content objects can be offered through IMS libraries and sold to users, thus involving others in the production and reducing the cost per student to produce the content. Reusable and new content objects can be combined easily in the same program.
IMS Libraries: Searchable and viewable libraries of content materials will be collected. The meta-data tag will be the method used for the search. All types of materials for all grade levels and training needs will be found in the libraries. Medical, defense, industrial, K-12, and higher education content will be available. The libraries may also contain full programs and modules which can be bought or leased according to the producer’s wishes.
Multiple Software Platforms: A number of companies are developing software that will support the IMS system. The software will continue to work on the meta-data tagging system.
Based on International Standards: The IMS system is based on internationally accepted standards which are sponsored by the IEEE (an international engineering group), EDUCAUSE, and a other organizations. The standards have also been accepted by the IMS Developers’ Group which is composed of a large number of software companies which are developing the software to support the standards. Members of this group include IBM, Apple, Oracle, Asymetrix and other major companies.
A number of prominent universities are also members of the group.
Individualize Materials for Students: While materials can be individualized for classes whose students have the full range of learning styles and intelligences, the material can also be personalized for students with fewer skills, smaller foundations of content knowledge, looped classes where two grades of students are mixed, and other non-traditional educational scenarios. Materials can be selected so that the needs of students can be met who are ahead or behind the rest of the class.
Web Based: While the materials will be available through the Web IMS libraries, and IMS software will be Web based, students will be able to directly access lessons on the Internet or through a LAN server at their school or work. Materials could be downloaded and saved to one classroom computer.
Promotes Self-Directed and Collaborative Learning: While the IMS system will track the students’ progress through the material, it will also promote self-directed learning and inquiry learning if the instructional designer includes activities to do this. The right activities will promote collaborative learning as students work on projects together through the IMS software provided by vendors.
Promotes the Use of Facilitated Learning by Faculty: While the faculty can be instrumental in preparing the lessons for students, when students work through the lessons, the faculty of necessity moves into the realm of facilitating learning. This is a major feature for educational institutions that have struggled with the integration of technology into the classroom and curriculum. New Methods to Meet New Learning Needs
For hundreds of years, most teachers and trainers have presented information to learners in one format—lecture to a whole class. That model has been identified as objectivist and is based on Skinner’s behaviorist learning theories where the teacher sets the pace, selects the textbook and resources, and controls the classroom. As students, we sat in seats and have been the target of those seeds broadcast by the objectivist learning theories and the linguistic teaching style. This is the teacher centered learning model, the hope was that the seed would fall upon fertile ground, germinate, and prosper.
For any organization teaching this way, recent research has shown that many learners not only prefer, but need, non-lecture styles to efficiently and effectively learn. They need styles with relevant interpersonal interaction, significant hands-on opportunities, well executed visual-spatial content, and with more self-direction of the pace and path of learning for each style of delivery. This is called constructivist at the K-12 level, and Andragogy at the adult level. In this model, the instructor facilitates the learning, identifies potential resources, and encourages the students to set their own rules, goals, and objectives. Learning contracts may be used to set the scope of work and assignments rather than a typical syllabus. This model is student/learner centered.
Gardner’s Multiple Intelligences (MI)
The theory of multiple intelligences (MI) was first described by Howard Gardner in Frames of Mind (1983). Howard Gardner is Professor of Education at Harvard University and holds research appointments at the Boston Veteran's Administration Medical Center and Boston University School of Medicine. Gardner defines intelligence as "an ability or set of abilities that allow a person to solve a problem or fashion a product that is valued in one or more cultures". His most current research indicates that there are eight distinct forms of intelligence: linguistic, logical-mathematical, spatial, kinesthetic, musical, interpersonal, intrapersonal, and the naturalist. Gardner suggests that different intelligences may be independent abilities--a person can be low in one domain area but high in another. All of us possess the intelligences but in varying degrees of strength and skill.
Intelligence Quotient (IQ) theory (based solely on the linguistic and logical-mathematical intelligences) assumes that a person's intellectual potential is a fixed, genetically determined trait, which can be measured early in life and will determine an individual's potential. Gardner's above definition suggests a broad view of cognitive functioning and is in sharp contrast to intelligence as defined by IQ. In other words, Gardner's MI model broadens our perceptions of what is meant to be intelligent. Until Gardner's arrival, this model of intelligence was perceived as the norm throughout most of the world. The MI theory continues to open the minds of educators, psychologists, learners and parents as to how learning and education can be changed so that all persons may be guided to achieve their maximum potential.
It is tempting to equate learning styles and intelligences because there are similarities, but until we have a much better understanding of both, it is best to avoid mixing the models.
Linguistic Intelligence - using words effectively. These learners have highly developed auditory skills and often think in words. They like reading, playing word games, making up poetry or stories. They can be taught by encouraging them to say and see words or to read books together. Tools include computers, games, multimedia, books, tape recorders, and lecture.
Logical-Mathematical Intelligence - reasoning, calculating. Think conceptually, abstractly and are able to see and explore patterns and relationships. They like to experiment, solve puzzles, ask cosmic questions. They can be taught through logic games, investigations, and mysteries. They need to learn and form concepts before they can deal with details.
Visual-Spatial In telligence - think in terms of physical space, as do architects and sailors. They are very aware of their environment. They like to draw, do jigsaw puzzles, read maps, daydream. They can be taught through drawings, verbal and physical imagery. Tools include models, graphics, charts, photographs, drawings, 3-D modeling, video, videoconferencing, television, multimedia, texts with pictures/charts/graphs.
Musical Intelligence - show sensitivity to rhythm and sound. They love music, but they are also sensitive to sounds in their environments. They may study better with music in the background. They can be taught by turning lessons into lyrics, speaking rhythmically, tapping out time. Tools include musical instruments, music, radio, stereo, CD-ROM, multimedia.
Bodily-Kinesthetic Intelligence - use the body effectively, like a dancer or a surgeon. Keen sense of body awareness. They like movement, making things, touching. They communicate well through body language and can be taught through physical activity, hands-on learning, acting out, role playing. Tools include equipment and real objects.
Intrapersonal Intelligence - understanding one's own interests, goals. These learners tend to shy away from others. They're in tune with their inner feelings; they have wisdom, intuition and motivation, as well as a strong will, confidence and opinions. They can be taught through independent study and introspection. Tools include books, creative materials, diaries, privacy and time. They are the most independent of the learners.
Interpersonal Intelligence - understanding, interacting with others. These students learn through interaction. They have many friends, empathy for others, street smarts. They can be taught through group activities, seminars, and dialogues. Tools include the telephone, audio conferencing, time and attention from the instructor, video conferencing, writing, computer conferencing, E-mail.
Naturalist Intelligence - demonstrates expertise in the recognition and classification of numerous species – the flora and fauna – of the environment. Value is placed on these individuals who can recognize members of a species that are especially valuable or notably dangerous and can
appropriately categorize new and unfamiliar organisms. These abilities come into play more probably with respect to “artificial” items. Discrimination by a teenager with regard to sneakers, cars, sound systems, or CDs also fits the intelligence. To help the categorization process:
? Verbal/Linguistic plays with words ? Logical/Mathematical plays with questions ? Visual/Spatial plays with pictures ? Musical/Rhythmic plays with music
? Bodily/Kinesthetic plays with moving ? Interpersonal plays with socializing
? Intrapersonal plays alone? Naturalist plays with categories
Canfield Learning Styles (LS)
Everyone has multiple learning styles. Dr. Albert A. Canfield created a learning styles (LS)
inventory as most people do not know what their LSs are or that their styles differ from others. There is
no one right or best LS. Our styles of learning, if accommodated, can result in improved attitudes
toward learning and an increase in productivity, academic achievement, and creativity. We use some styles when learning, but we tend to prefer a small number of instructional methods. Furthermore, evidence indicates that an individual can learn better, smarter, faster and retain more information when material is presented in one’s preferred learning style/multiple intelligence. However, research does
not support that there will be one right method to teach to a student.
Humans learn in a variety of ways and it is likely that there are ways that have yet to be discovered along with the instructional methods to meet them. Presenting information to students in only one LS does not meet all of the student’s needs. A student might have roughly the same preference for learning content through visual and hands-on materials. If the content is presented only to the visual preference, the student would not learn as completely as he/she would if the content was presented
by hands-on methods. All students learn differently due to a dominant or preferred LS.
The Canfield LS instrument is easy to use and self-scoring. It provides students with knowledge about their individual LS is and how they differ from others. The instrument is paper based and takes about 30 minutes to complete. It can be submitted before the class begins or the institution may keep the LS scores of all students on record and make them available to instructors. The instrument helps determine preferred learning conditions, areas of interests, modes of learning, and course expectations. Conditions
Peer: Working in teams; good relations with others; having friends; feeling positive about working and building something together. Clearly a high priority in an organization: Work logically and clearly organized; meaningful assignments and sequence of activities.
Goal Setting: Setting one’s own objectives; using feedback to modify goals and procedures; making one’s own decisions on objectives. This is an important element of being self-directed and proactive. They need to know how they fit in with the larger company goal.
Competition: Desiring comparison with others; needing to know how one is doing in relations to others. America fosters this - but competing does not automatically foster excellence. Competition is
an extrinsic reward ... it is better replaced with an intrinsic reward system.
Instructor: Knowing the instructor personally; having mutual understanding; liking one another. Give plenty of eye contact and positive non-verbals.
Detail: Specific information on assignments, requirements, rules, etc. People who want minimal amounts of detail are right brain conceptual thinkers. They need to understand the concept first ... and then will sit through the detailed explanation - remembering only the details that are important to their conceptual understanding. People who want details in a sequential order are left brain linear thinkers. Take them through the process in an orderly, chronological process.
Independence: Working alone and independently; determining one’s own study plan; doing things for oneself.
Authority: Desiring discipline and maintenance of order; having informed and knowledgeable instructors and superiors.
Content
Numeric: Working with numbers and logic; computing; solving mathematical problems; etc. Provide with charts, spreadsheets.
Qualitative: Working with words or language; writing, editing, talking. Provide a report to them prior to a meeting or the need to make a decision. Lengthy question and answer periods will give them time to formulate the idea in their own words
Inanimate: Working with things; building, repairing, designing, operating.
Provide a physical model or way to work with the idea in question
People: Working with people; interviewing, counseling, selling, helping.
Mode
Listening: Hearing information; lectures, tapes, and speeches.
Reading: Examining the written word; reading texts, and pamphlet.
Iconic: Viewing illustrations, movies, slides, pictures, and graphs.
Direct Experience: Handling or performing; shop, laboratory, field trips, practice exercises, and hands-on.
Expectancy Score: The predicted level of performance.
Variance in One Class
It is quite useful to show a class all of the scores from the inventory as it helps them to understand that everyone learns differently and that no one way of learning is best (See Table 1). This also helps instructors to think about the LS class mix and how to create activities and assignments that will meet a variety of styles. It provides support for instructional designers who need to learn new ways to design instruction for classes with multiple LS and intelligences. Figures are shown in percentiles so that a score of 95 would indicate the 95th percentile and the learner would have a high need to have this style met. A score of 05 would indicate the fifth percentile – or a low need for the learner.
The Canfield is quite useful in distributed learning as it shows scores for an instructor to provide structure, the independence level of the student, and the need for an authority figure by the student. If the student instructor percentile score is 25, this indicates a low need for an instructor to provide structure; 85 would indicate a much higher student need to have structure imposed from the outside.
If the student’s independence score is high, such as an 85 or 95, it indicates that the student is quite independent in learning and probably capable of working well in a distributed learning environment. A low score would indicate that the student is not an independent, self-directed learner and that the instructor will need to provide an intervention to help the student begin movement toward this goal. Students need time to move through the process of becoming independent learners. This may take six months to a year.
If the student’s authority figure is low, they are likely to function well in a distributed learning environment and collaborate well with other students. If the figure is high, they require an authority figure to provide the structure and set the rules. The instructor will need to provide an intervention that will the student’s reduction of the need for authority imposed from the outside.
These three scores are useful as indicators for the distributed learning instructor of students who will do well in the environment, and those who need interventions to begin movement toward self-directed learning. It is always a temptation to want to help the needy student and provide the structure and authority that they often demand in an instructor. These demands are usually another indication that the student is unable to be self-directed and independent in their learning.
Table 1: Sample Class Learning Style Scores (All Figures are Percentiles)
Student#1#2#3#4#5#6
Conditions
Peer657588824747
Organization206030999982
Goals455235121503
Competition750737370510
Instructor258338941065
Detail175345659990
Independence 885557450140
Authority651065180810
Content
Numeric727252532290
Qualitative304090603030
Inanimate301255209090
People526295147105
Mode
Listening258073651295
Reading556340609047
Iconic 252507800108
Direct
Experience882587109810
Expectancy979575605989
Table two shows the preferred learning style of the population by percent and age. Observe that the linguistic Intelligence increases with age according to Teele’s research while the bodily-kinesthetic intelligence shows a reduction.
Table 2: Teachers’ Dominant Intelligences by Age (1995, Teele)
Ages:20-2526-3536-4546-5556+
Linguistic 3.51 3.94 4.22 4.58 4.37
Logical-Mathematical 3.07 3.13 3.06 3.26 3.41
Spatial 4.33 4.34 4.37 4.21 4.15
Musical 3.17 3.36 3.33 3.38 3.33
Bodily-Kinesthetic 4.85 4.40 4.21 3.90 4.11
Intrapersonal 3.13 3.37 3.52 3.69 3.40
Interpersonal 5.80 5.43 5.27 4.95 5.08
Litzinger/Osif LS Theory
Litzinger & Osif (1992, 73) chose to look at LS in differently and describe LS as "the different ways in which children and adults think and learn." They see that each develops a preferred and consistent set of behaviors or approaches to learning. They break the learning process into three segments:
1.Cognition--how one acquires knowledge
2.Conceptualization--how one processes information. There are those who are always looking for
connections among unrelated events. For others, each event triggers a multitude of new ideas.
3.Affective--people's motivation, decision making styles, values and emotional preferences will
also help to define their learning styles.
Kolb's LS Theory
A number of people have tried to catalog the LS ranges in more detail than this. Kolb is perhaps one of the best known. Kolb showed that LS could be seen on a continuum running from:
1.Concrete experience: being involved in a new experience
2. Reflective observation: watching others or developing observations about own experience
3. Abstract conceptualization: creating theories to explain observations
4. Active experimentation: using theories to solve problems, make decisions
Hartman (1995) took Kolb's LS and gave examples of how one might teach to each style:
1.Concrete experiencer--offer laboratories, field work, observations or trigger films
2.Reflective observer--use logs, journals or brainstorming
3.Abstract conceptualizer--lectures, papers and analogies work well
4.Active experimenter--offer simulations, case studies and homework
Although Kolb thought of these learning styles as a continuum that one moves through over time, usually people come to prefer, and rely on, one style above the others. And it is these main styles of which instructors need to be aware when creating instructional materials.
Implications for Instruction
Where do these lists of learning styles lead us? There are probably as many ways to "teach" as there are to learn. People do not see, hear, or experience the world in the same way. They have very different preferences for how, when, where and how often to learn.
Using multiple types of media (video, audio, data) ensures that all learning styles are met and that significant methods for interaction are provided. This mix of media is available now. With it all learning styles can be reached. It also includes a component that enables students to become self-directed learners and reduce their sense of isolation.
The synergy of technologies available through multiple media and the Internet creates new learning opportunities for adapting learning to learning styles. Annual growth in web-based training, as projected by International Data Corporation, is to be 150 percent per year through the end of the century, as compared to eight percent per year for instructor led training. Unfortunately, this effective use of technology is not happening. Of the 23 sites offering interactive web-based training from software/hardware developers or training developers (such as Microsoft, Ziff Davis, Gartner Group, and Digital Think), none of those providers personalizes training or education by preferred learning style, nor was that criteria even contemplated as a quality measure in the research (Kisa Harris, ED Journal, March 1998). We have arrived at a place where we need to rethink and reinvent how we use learning styles and multiple intelligences to teach effectively using technology.
Most assessments are pencil and paper based and as such are not authentic assessments. They do not assess the learner in the act of doing something, but instead ask the learner to make a choice about what they might do in a given situation. This is not ideal.
Personal Learning Model (PLM) and Profile (PLP)
The Problem: Students learn in a variety of ways. Technology and mediated courses can meet these needs better, however, there is currently no authentic assessment tool that works in an online environment. There is currently no model or tool capable of assessing a student’s learning styles, multiple intelligences and media preferences.
The creation of a theory, model and tool will have a profound effect on the adoption of mediated courses because of its interactive, diagnostic, and multiple use components.
The Personal Learning Model and Profile: The Education Coalition, a nonprofit technology consulting group, a for-profit corporation specializing in assessing student performance, has developed and tested the concept for a Personal Learning Model to assess the learning styles, multiple intelligences and media preferences of distance learners. The assessment is called the Personal Learning Profile (PLP) which is in development.
Priority Need: The PLM/PLP can be used by any distance educator seeking to develop online courses prescriptive to the needs of different types of distance learners. The PLP assessment tool can be taken anytime or anywhere by a student and will capture their learning needs and create a Personal Learning Profile. The assessment results and the personal profile of the student will be sent directly to the instructor for review and to the instructional management system of the organization for the learner’s portfolio. This entire process will occur online in a virtual reality environment. Instrumentation Strategy: The partnership is developing an online environment capable of assessing a student’s learning styles, multiple intelligences and media preferences. It is an authentic assessment as compared to the paper/pencil assessments currently in use. The student will be tracked while moving through multiple tasks developed to specifically assess learning preferences, with results provided to the student and the instructor.
The Personal Learning Model has been developed based upon the learning styles research of Canfield and Gardner’s multiple intelligences work. It also draws heavily upon the work of TEC in the application of these academic theories and practices to the use of media for instructional purposes and student media preferences.
The basis of the model is that technology provides a deeper learning experience for students if content materials are presented in their preferred style first, then in his/her second and third styles. Mediated learning materials assist students in learning more quickly and are more engaging for them than traditional delivery methods. The importance of the model is that it will provide individualized learning, although several dozen or hundred distance learners might be enrolled in the same online course. The Personal Learning Profile, generated by the model, will enable educators to plan
effectively and modify curriculum and assignments to contend with each student’s individual learning style differences.
Using learning styles for courseware design is gaining national attention and has become a “significant movement” in the training industry (Advanced Distributed Learning Initiative, 1997). Software programs to manage instruction are being developed and marketed by a number of leading vendors and will be deployed throughout educational institutions across the country. Yet none are being developed to assess the student’s learning preference, which would make the online courses more effective. The Personal Learning Model fills this void.
The Personal Learning Profile is the application that will provide the assessment results for the student and instructor. After the assessment, the student’s preferences will be embodied in a meta-data tag which will always be sent to the instructional management system when the student logs on. In response, the Instructional Management System will create an individualized Web page to deliver materials and content to the student in the student’s primary, secondary, and third learning preferences. No other program is being developed to do this, although such a tool is crucial to the next generation of technology for distance learners.
Regional and National Consequences of a Successful Project
Adoption of the Personal Learning Model/Profile are innovative ways for regional and national post secondary institutions to leverage the monies already invested in distance education by delivering prescriptive learning applications.
Goal: To develop fully interactive, high quality asynchronous courseware for anytime, anywhere assessment of a student’s learning/intelligences style strengths and place it on the Internet for use by distance learners and distance educators.
Objective 1:Create one Personal Learning Model assessment tool which will assess at a minimum three types of learning styles and the learning modality of students in six areas: visual, auditory,
math/logic, musical, interpersonal, and intrapersonal.
Objective 2:Build into the Personal Learning Model one kinesthetic and tactile assessment component through software development and integration of virtual reality and generate the beta version of the Personal Learning Profile.
Objective 3:Field-test the Personal Learning Model and Personal Learning Profile in a minimum of six distance learning environments including K-12, higher education, and training.
Problem Solution Anticipated Results
No existing software applica-tion that works in a learner-centered method, defines the student’s personal learning model, and defines that model through dominant learning style, instructional media, and instructional methods.Personal
Learning Model
and
Personal
Learning
Profile for
online use
Nationally accepted standardized distance learning
assessment tool that is interactive, diagnostic, and
suitable for multiple users that can be used anytime,
anywhere by any distance learner. Qualitative
measurement tool that will contribute to the
improvement of online learning.
Meets standards set by EDUCAUSE and the IMS
Project (Instructional Management System)
The important thing is to recognize that not everybody is comfortable and productive within the same learning style mold. To engage and better instruct learners, one must first determine the individual’s learning predominant and secondary learning styles, and then use technology to adapt learning to learning styles.
Strategies for Implementation and Adoption of Distance Learning
While distance learning has been an education and training fixture for decades, there is still little understanding of the entire field of change as it relates to the implementation and adoption strategies which are applied to distance learning. While many organizations embrace distance learning (aka distributed learning and e-learning), there are varying levels of implementation and adoption. Some organizations fully use all types of distance learning technologies and all staff and faculty have fully adopted their use. Other organizations have mixed implementation and adoption patterns.
Varying uses levels, adoption levels, and implementation levels of educational technologies lead one to ask why we are still seeing less than a full implementation and adoption of technologies that are perceived as useful to many users.
The adoption of the innovation of distance learning has a substantial history of research into the adoption barriers. Generally, they have been broken down into general and psychological barriers. Implementation strategies have received less study, but literature reviews show clear patterns where strong implementation strategies have not been planned and/or followed by organizations.
Funding, ongoing professional development for instructors, limited equipment and access to networks, and a naiveté that leads to ignoring the political realities of the organization are some of the root causes for less successful adoption of distance learning. The lack of needs assessment documentation, strategies for adoption, planning instruments, and involving the entire organization in planning are also problems.
While many organizations seem to have taken a careless approach to implementation and adoption of distance learning, there are clearly many other organizations whose leaders are clearly ignoring the change in education and training on all fronts. Instructors are still using traditional teaching methods, no technology exists in the classroom except chalk and a blackboard, instructors do not use computers, e-mail, or have any idea how to present information to students whose learning style may not match their teaching style.
Many of these now point at the educational technology "no significance difference" research as the reason not to use educational technologies. They ask, "Why spend the money, if there is no difference in learning?" A sound management approach, but it is based on a misinterpretation of the research. They are not looking at the use of multiple educational technologies research which does show a significant difference in learning. Learners who have access to resources presented through a variety of learning technologies do show improvement.
General Barriers to the Use of Educational Technology
A number of barriers to the use of educational technology have been identified in recent years. They include:
Lack of information about technology (Baer 1978)
Length of time for widespread use (Baer 1978)
Inappropriate match - technology and service (Lucas 1978)
Panacea approach with technological solution (Benne 1975)
Machine mysticism (Pacey 1983)
Misperception: Technical advance leads progress
Myth: Cultural lag occurs everywhere as we try to keep up with progressive technology
Use technology to answer new patterns of problems
Lack of money (Dirr in Barron 1987)
Lack of faculty commitment (Dirr in Barron 1987)
Lack of trained support staff (Dirr in Barron 1987)
Faculty Concerns (Barron 1987)
Class size
Discussion and face-to-face involvement
Lack of support for faculty from peers/instructors
Hands-on experience for students in subjects such as chemistry
Reality: More students are performing chemistry and physics experiments before computer keyboards instead of laboratory benches.
Lack of face-to-face interaction
Perception: Benefits are assumed to accrue from personal interaction
Psychological Barriers to the Use of Educational Technology
A number of psychological barriers to the use of educational technology have been identified. In addition to “It’s never been done that way before,” other psychological barriers include suspicion and fear of change as well as telephobia which is a suspicion of change which involves television. Others fear that they will make a fool of themselves in front of their peers. “People who have watched TV for 20 years have built up all kinds of cultural expectations about people...on the screen. They expect to see a polished performer reading a script without a hair out of place. In contrast, executives or managers on a videoconference
tend to have their ties askew, don’t always look at the camera ..and seem unsure of what to say” (E. C. Gottschalk, Jr., Wall Street Journal). Goldstein (1991) says that he is certain that the “move from the tutorial to the lecture that accompanied the rise of the modern university was greeted with similar outcries. I am equally certain that the differences in learning outcomes are as overstated today as they were then.”
Education television and videoconferencing have been categorized as only hype or show biz and there is still an unsubstantiated fear that television may only entertain rather than inform. “As long as that attitude exists, teleconferencing will be limited to that use...there must be a recognition that teleconferencing is used not in a show biz environment but in a day-to-day environment, married to applications” (Jack Fox, Western Union).
Distance learning is perceived as being somehow fundamentally different from traditional instruction. What is the difference between a live lecture delivered to 600 students in a campus lecture hall and the same lecture delivered over a telecommunications system? This is an “intellectual trap” that leads us to believe that distance learning is so inherently different from what we have come to define as traditional instruction that it demands entirely different rules or it cannot possibly meet the established standards and therefore it is not worth fixing (Goldstein, 1991).
While distance education has become well established, there is still skepticism within the academic community about whether this form of education is of comparable quality to the more familiar classroom-based learning, as well as opposition from those who regard it as a threat to traditional faculty roles and classroom enrollments (Reilly & Gulliver, 1992). As long as regulators and accreditation agencies continue to apply measures intended for classroom-based instruction to distance education, the skepticism will be reinforced. This uneasiness with distance education is heightened by the sense of a “competitive threat from the entry of an ‘outside’ institution into a state. The ‘Not Invented Here’ syndrome reflected in this response is one of the greatest potential barriers to the national expansion of distance education” according to Reilly and Gulliver.
Others noted that TV does not transmit a personal high touch environment, but is a cold, high tech medium which looses body language, chemistry, electricity, does not maintain a long attention span, is not interactive, is known for low quality. Educators have noted that it lacks central grading, testing and measurement elements.
The advantages of educational technology have been noted as being cost efficient, providing access to programming and having the ability to enrich education (Seidman,1986; Wilson, 1987; Lewis, 1985).
Strategic Planning to Implement Distance Education Programs
The literature lists major barriers to implementation (Pearson, 1990). Lack of successful institutional planning for the delivery of distance education programs at educational institutions represents a major barrier to implementation and success. The problem is that there was no validated process for planning for implementation of successful distance education programs. Pearson’s study determined what critical factors leaders of successful distance education programs considered to be important prior to, during, and following implementation of the program at their institution.
Thirty administrators in education, distance education specialists and program providers were invited to participate in a three round Delphi to determine the 20 critical factors that should be considered in the planning process to implement a distance education program at an educational institution. The 30 key leaders were asked with each Delphi round to refine and rank those critical factors that they listed. The final round produced 20 critical factors in rank order.
Table 3: Critical Factors in Rank Order
1.Identified need (perceived or real) for the program.
2. Faculty and teachers supportive and given incentives for motivation.
3.Funds for capital costs; production, equipment, facilities.
4.Availability of on-going money for operations and expenses.
5.Quality of the educational content of the program (evaluation).
6.Adequate support staff to produce the program.
7.Ensuring equivalent learning experience to remote students.
8.Enthusiasm and belief by the institution in the overall distance education project.
9.Identification of a visible, spirited key leader/administrator initiating program.
10.Adequate receive sites, facilities, and staff.
11.Availability of appropriate and specialized equipment to deliver the programming.
12.Sufficient time for careful needs analysis; Identify range of services and programmatic
needs of students. Example: Number of people, type of courses, ages served, location.
13.Ensuring equivalent status for remote students: i.e., credit, degree.
14.Instructional design/TV production; interactive components, length, frequency, number.
15.Identification of a marketing plan for the network, system or program.
Public relations with the public.
16.Cost effectiveness: feasibility and justification for delivery system to students/institution.
17.Identified/gathered support/partners for program: industry, corporate, legislative, institutional.
18.Ensure continued program credibility with the public, faculty, students, and supporters.
19.Knowledge of educational administrators, teachers and staff at educational institutions on
what distance education is and how to teach and use it effectively.
20.Ability to accredit courses, offer credit or transfer credit across states or institutions.
Panelists indicated that the factors were dependent upon each other for the ultimate success of the program implementation. The critical factors they generated contained a
planning model which included the steps of purpose, philosophy, organizational structure, people, finances, equipment and facilities. Experts indicated that successful implementation depended upon the completion and thorough investigation of each critical factors.
The model set a high priority on human and fiscal resources that can serve as a model for the strategic planning of administrators of new programs in long distance instruction. Planning
for the implementation of the program requires a major investment in time, people and funding. Serious consideration should be given to the number one critical factor: "identification of the need for the program." All the experts agree that without this identified need, an institution should not move ahead to purchase equipment, hire people, or even think about delivering a long distance program. Faculty involvement, incentives, motivation and training were ranked
as serious issues for these successful institutions. According to these experts, the educator
is a high priority in the delivery of long distance coursework. While the fear of teachers being replaced by the technology appears to be an overriding concern and barrier for many institutions, the importance of the teachers remains critically high in the electronic classroom.
Table 4: The Strategic Plan - Steps for Change
I. Decide to Plan for Change: Awareness
1.Key Administrators
2.Super Leader
3.Understand Elements of Change
a)Flexible Environment
b)Policy
c)Philosophy
d) Leadership
II. Recognize a Real Need vs Perceived Need: Interest
1.Identify the Recipient
2.Why Have the Program? Who wants and who needs the program?
3.The Competition: Who Else Is Doing It?
4.Is the Program Really Needed?
III. Understand the Real Reason for Implementation: Advantage
1.Value to the Organization
2.Political Issues Involved
3.Technology or Need Driven
https://www.doczj.com/doc/b717751522.html,petition Driven for Competition's Sake
5.Philosophy of the Program
6.Culture of the Organization Affects the programs:
Political issues involved
IV. Mission of the Organization: Evaluation
1.Does the Programming Fit the Organization’s...
Goals, Objectives, Quality Standards
2.How Will This Help the Organization? If it won't, don't!
3.What is the Driving Force to Market the Program?
4.Will it Make Money?
5.Will It Be Self Sufficient?
6.How Large Do We Want It to Become?
7.What Is the Return on the Investment ?
V. Plan the Program: Trial
1.Time - Take the Time to Plan
2.People - Faculty/Staff
3.Space, Facilities, Equipment
4.Production Capability
5.Money - Now & Later
VI. Review What the Organization Does Now: Observability
1.Will Distance Learning Duplicate Services?
Classes, Staff, Departments
2.Is the Organization Working Well In Training & Education
3.Does the Organization Support Education & Training, Change, Technology
4.Do We Have Enough People and Support to Add Change?
5.What Are the Organization’s Strengths and Weaknesses
VII. The Gap: Compatibility
1.How Far to Go to Have a Successful Program
2.Will the Organization Be Able to Change
3.Subtract the Difference Between....Where We Want to Be
- Where We Are Now
4.Can We Do It?
VIII. Contingency: Pre-adoption
1.Trial & Pilot
2.Flexibility
3.Client Needs
4.Institutional Perceptions
5.Success vs. Failure. What happens if...it won’t, doesn’t, can’t, or if it is better or
different
IX. Implementation: Adoption Commit to the Ongoing Process
1.Lead People
2.Design Programming
3.Train in...Production techniques and Technology
4.Faculty Support
5.Dollar Support
6.Continued Resources - Finance the Program
7.Plan for Change, Growth and On-going Growth
8.Believe in the Program
9.Garnish Support Again and Again
10.Evaluate the Program
Conclusion
Follow: All Steps of Change and All Conditions of Success
Interpretation of Innovation Adoption by CBAM (Concerns Based Adoption Model) There are seven Levels of Use identified in the Concerns Based Adoption Model (CBAM),
and staff who are adopting an innovation will move up these levels in seven different areas. CBAM is used to determine at which level of use teachers and staff are working. By determining their level of use and the time each takes to move through the levels, it may be possible to plan an implementation strategy that will reduce the time to adopt the innovation of distance learning, and specifically project programming. The levels and areas are as follows. Table 5: CBAM Levels of Use
Levels of Use Description
0: Non-use State in which the user has little or no knowledge of the innovation, no
involvement with the innovation, and is doing nothing toward becoming
involved.
I: Orientation State in which the user has recently acquired or is acquiring information
about the innovation and/or has recently explored or is exploring its value
orientation and its demand upon user and user system.
II: Preparation State in which the user is preparing for first use of the innovation.
III:Mechanical Use State in which the user focuses most effort on the short term, day-to-day
use of the innovation with little time for reflection. Changes in use are
made more to meet user needs than client needs.
IV A: Routine Use of the innovations stabilized. Few, if any, changes are being made
in ongoing use. Little preparation or thought is being given to improving
innovation use or its consequences.
IV B: Refinement State in which the user varies the use of the innovation to increase the
impact on clients within immediate sphere of influence. Variations are
based on knowledge of both short and long term consequences for
clients.
V: Integration State in which the user is combining own efforts to use the
innovation with related activities of colleagues to achieve a collective
impact on clients within their common sphere of influence.
VI: Renewal State in which the user re-evaluates the quality of use of the innovation,
seeks major modifications of or alternatives to present innovation to
achieve increased impact on clients, examines new developments in the
field, and explores new goals for self and the system.
Areas of Concern to Address for Instructors
?Instructor preparation time for telecasts and pre/post activities
?Conflicts in scheduling the program (time zone and organization activities)
?Knowing if the distance learning program is having a positive impact on students ?Conflicts between using the distance learning program and other instructional programs ?Coordination of use of the distance learning program with other staff members ?Understanding the instructor’s role in using the distance learning program
?Shared equipment and sharing limited instructional materials for distance learning program ?Use problem solving techniques to adjust the distance learning program use to best meet student needs and use available resources
Table 6: How Concerns May be Expressed at Each Level of CBAM
Type of
Concern
Stages of Concern Expression of Concern
Self0: Non-use Awareness:
I am not concerned about it (the innovation).
I: Orientation Informational:
I would like to know more about it.
II: Preparation Personal:
How will using it affect me, students, department, and
organization?
Task III: Mechanical Use Management:
I seem to be spending all my time getting materials ready.
Impact IVB: Routine
IVB: Refinement Consequences:
How is (my) use affecting students?
V: Integration Collaboration:
I am concerned about relating what I am doing with what other
instructors are doing.
VI: Renewal Refocusing:
I have ideas about something that would work even better.
References
Hall, Gene, and Loucks, Susan. The Concerns Based Adoption Model (CBAM)
Lane, Carla (1998) Gardner’s Multiple Intelligences https://www.doczj.com/doc/b717751522.html,/eddevel/gardner.html
Pearson, Virginia W. (1989). "Critical Factors considered in the Planning for the Administration and Implementation of Long Distance Interactive Video Instruction." Oklahoma State University
Teele, Sue (1995). Teele Inventory of Multiple Intelligences" from The Multiple Intelligences School: A Place for
All Students to Succeed, Citrograph Printing, Redlands CA, 1995, p. 36.
多输入多输出系统(MIMO ,Multiple input multiple output)最早是控制系统中提出的一个概念，它表示一个系统有多个输入和多个输出。而MIMO技术早期用于干扰无线信号，后来则用于移动通信和固定宽度的无线领域。如果将移动通信系统的传输信道看成一个系统，则发射信号可看成移动信道(系统)的输入信号，而接收信号则可看成移动信道(系统)的输出信号。在通信中，由多径引起的衰落通常被认为是有害因素，不过对于MIMO系统而言，多径可引起的衰落以作为一个有利因素并加以利用。MIMO 技术以其可以有效利用多径引起的衰落来成倍地提高业务传输速率，并引发了通信的一次革命。基于通信系统中的MIMO技术的使用情况，近几年国外学者提出了MIMO雷达的概念。 1.MIMO雷达信号处理发展历史 1.1 国外研究现状 国外最早在MIMO雷达信号处理领域开始开创性的工作者有New Jersey Institute of Technology的Eran Fishler、Alex Haimovich等人，他们研究的工作主要集中在MIMO雷达的信号建模，从模型中获取我们感兴趣的参数的算法研究（如散射点的散射系数，散射点距雷达的距离等），并从雷达对目标检测性能等方面说明它相对于普通的相控阵雷达所具有的优越性，明确指出了MIMO雷达将是未来雷达发展的一个趋势。几乎在同一时期，MIT Lincoln Laboratory的K. W. Forsythe等人的研究工作也在同步进行，他们的研究工作则主要集中在MIMO雷达的性能优越性的理论证明。同时该实验室的Frank C. Robey也作了大量的实验，通过大量实验证明MIMO雷达相比传统的雷达有许多优点。 目前国外研究MIMO雷达的著名机构有美国的佛罗尼达大学(University of Florida)，MIT Lincoln Laboratory、新泽西理工学院（New Jersey Institute of Technology）等。研究的焦点主要集中在有源MIMO雷达波形设计、多通道信号模型、目标检测性能分析、DOA估计性能分析等方面，并取得了巨大的成果，Bliss, D.W. Forsythe, K.W.，率先在理论上证明了MIMO雷达技术在目标角估计、降低旁瓣电平及目标检测性能优于常规雷达。在有源MIMO雷达波形设计方面和DOA估计性能分析
<涉及到外部变量的声明>（不是定义，因为不能赋值） c/c++语言中有很多地方要用到extern，但是如果没有真正的了解它的意义，会给编程带来很大的麻烦，为了使大家少走弯路，特祥细的说明一下。 对于比较小的程序，一般只有一个c文件和一个头文件,全局变量我们通常会直接定义在c文件中，在程序之前加int i定义。如果要在头文件中定义有以下两种方法：用extern来声明:extern int i;这一句只是对变量i进行声明，在c 文件的程序之前必须加上int i进行定义。extern int i=0;这一句声明和定义都做了。 对于大一点的程序，有很多c文件和头文件，这个时候全局变量就必须在头文件中声明(不需要初始化)，然后在一个c文件中定义(该初始化的要初始化)。如果在头文件中定义，则编译的时候会出现重复定义的错误。如果只有头文件中声明就会出现没有定义有警告。 *** ERROR L104: MULTIPLE PUBLIC DEFINITIONS SYMBOL: K MODULE: 222.obj (222) 出现上述错误则是因为变量k重复定义，把你的头文件中的变量定义前加extern(只是变量声明不用初始化)，再在某一个你要调用该变量的c文件的程序之前再定义 (注意第一个调用的c文件要负责附带初始化该变量，其他调用的c文件就不需要初始化过程啦) 首先纠正你的一个错误，extern是用来声明变量而不是定义变量的 当你需要在一个c语言文件中使用另外一个c语言文件中定义的变量时就需要加上extern来声明，这样编译器就知道这个变量是在别的文件中定义的。 比如： 你在foo.c中定义了一个全局变量int a=10,你在fff.c中想使用这个变量a，那么你就需要在使用之前在fff.c中用extern声明这个变量：extern int a； 1.extern的作用 extern有两个作用，第一个,当它与"C"一起连用时，如: extern "C" void fun(int a, int b); 则告诉编译器在编译fun这个函数名时按着C的规则去翻译相应的函数名而不是 C++的， C++的规则在翻译这个函数名时会把fun这个名字变得面目全非，可能是 fun@aBc_int_int#%$也可能是别的，这要看编译器的"脾气"了(不同的编译器采用的方法不
Visual-Spatial Think in pictures and images Good with spatial relations Learn through visuals “See” solutions Interpersonal Learn through interaction with others Can influence others’ opinions/beliefs Understand others’ motivations Comfortable with groups Bodily-Kinesthetic Well-coordinated Enjoy physical activity Use gestures and body language Musical-Rhythmic Emotions, intellect sensitive to music Understand musical rhythms, patterns Perform, create music Enjoy music Mathematical-Logical Use abstract symbols Enjoy working with numbers Learn through application of logic Rigorous, analytical, scientific thinkers
Naturalist Keen observers Aware of environment Can discern patterns well Learn though classification Learn though hands-on activity Intrapersonal Need time to process information Have strong opinions and beliefs Know themselves well Introspective Verbal-Linguistic Think in words Sensitive to rhythms of words Communicate well through writing Learn by reading, writing, discussion
复选题分析（Multiple Response）(2008-06-16 14:28:20) 标签：教育分类：SPSS 复选题分析（Multiple Response） 最近群里经常有朋友在问；“怎样用SPSS输入多项选择题啊？”于是本人整理以往学习资料，希望这些东西能够帮他们解决问题。 一、复选题分析的原理 （一）复选题分析的限制和用途 复选题在许多问卷或数据收集中经常出现，例如：某家旅行社询问采访者最近一年内，曾经搭乘国内四家航空公司（东方、南方、上海、海南）中的哪几家，这就是一个典型的复选题问题。通常这些复选题只是请受访者构选“有“或”无“，亦即该选项只能建立数据时，以命名尺度的”1“与”0“来呈现，但命名量表是精度最低的测量方式，其会限制这些复选题可使用的统计检验分析方法。 复选题经常被使用，但其通常使用的统计分析只有频次分析表与交叉分析表等描述性统计，且不能进行后续所有的检验分析，并且经常被滥用。如果进行非学术性研究，只想了解复选项目的频次分布，则可使用复选题；但如果进行学术性研究，则建议尽量不使用复选题，而尽量使用复选题的变形。例如：询问受访者乘坐各家航空公司的乘坐次数、询问每种减肥方式的使用比重、询问对每种兴趣的偏好程度或所花费的时间等，即将原来的命名量表测量变量变为等距或以上的量表。 （二）如何在SPSS中创建复选题 如何在SPSS中输入多项选者题以及如何进行频次和交叉分析，下面将举例说明。例如调查学生的“上网项目“和”嗜好“两组复选题目，其中“上网项目”包括找数据、网站购物、在线游戏、聊天室等；经常从事的“嗜好“包括打球、看电视、打电动、逛街、唱歌等。 复选题在建立数据文件时，必须将每一个选项设为一个变量，而非一组变量成为一个变量，例如：这里上网项目与嗜好各有4个和5个选项，合计9个选项，则需要新建9个变量，如下图所示：
说说几个小问题吧,一般我们在用KEIL的时候,只要编译器报 - 0 Error(s) 一般我们都不去管多少个Warning(s).了,一般这样程序基本都能运行, 但是其实仔细想想,这里还是有问题的,否则编译器没事吃饱了撑得,报什么警告啊~~~ 今天来说说*** WARNING L15: MULTIPLE CALL TO SEGMENT这个问题! 其实这个问题应该是引起注意的，有可能引起程序冲突，但是一般时候程序运行不会有问题,但是如果出来问题,那将会是很讨厌的问题. 分析一下产生这一警告的一个根源是：例如在主循环里调用了一个函数，而在中断服务中，你又一次调用了同样的函数。这样当主循环运行到该函数中时，一旦产生中断，则在中断里又再次调用该函数！而使得该子函数发生了重入,这时，尽管概率很低,但是很可能出错！这样,编译器就给出了警告!告诉你*** WARNING L15: MULTIPLE CALL TO SEGMENT ,表达的意思是发生了重入!字面意思自己理解去吧~~~ 想要避免这种情况的方法 一.用reentrant使函数重入 关于reentrant的说明： 1，重入函数不能传递bit类型的参数和变量； 2，重入函数建立的是模拟堆栈区,所以不使用一般函数位于存储模式默认空间的可覆盖式堆栈，而是在同一空间从顶端另行分配一个非覆盖式的重入堆栈。 small 默认空间是 data; compact 默认空间是 pdata; largr 默认空间是 xdata; 3，由于要保存参数和局部变量，所以会消耗很大的栈空间；尽量少用这种模式； 4、在同一程序中可以定义和使用不同存储器模式的重入函数，任意模式的重入函数不能调用不同存储器模式的重入函数，但可以调用普通函数。 5、实际参数可以传递给间接调用的重入函数。无重入属性的间接调用函数不能包含调用参数。 二.如果空间多的话，可以定义两个同功能的函数，分别在中断和中断外调用 别的方法没研究出来,嘿嘿~~~对了我建议用第二种方法好点,第一种有些限制,不爽~~
多剂量与单剂量比较(Multiple vs. Single Dose Comparison) 数据摘要： Fourteen healthy human subjects (Sub), seven male (M) and seven female (F), received a 1 g i.v. dose of Drug A for seven days. On Day 1and Day 7 of dosing, blood samples were taken from each subject prior to dosing and at 0.25, 0.5, 0.75. 1, 1.5, 2, 4, 6, 8, 12, 18, and 24 hours postdose. These blood samples were assayed for both plasma total drug and plasma unbound drug. From these assay values, the area under the plasma concentration vs. time curve (mg r/mL) from time 0 hours to 24 hours (AUC24) was estimated from the single dose (Day 1) and multiple dose (Day 7) assay values. The area under the curve from time 0 hours to infinity (AUCinf) was also estimated for the single dose (Day 1). 中文关键词： 多剂量,单剂量,对比.健康人,性别,药物, 英文关键词： multiple,single dose,comparison,healthy human,sex,drug,
Multiple Tort Feasors Ⅰ. Joint and Several Liability 1. Under the rule of joint and several liabilities, each negligent defendant is fully responsible for a plaintiff's damages, assuming that the defendants caused an indivisible harm. However, liability is several only where defendant cause distinct or separable components of a plaintiff's harm. 多重侵权行为 连带责任 在连带责任规则下，假定被告造成不可分割的损害,每个过失被告对原告的损害负全部责任。但是，只有在被告对原告造成不同或可分离的损害时，才是分别责任。 2. Should comparative fault states continue to apply joint and several liabilities? No consensus has emerged. In fact, jurisdictions are so splintered that the Restatement refused to endorse a position. Instead, the Restatement sets out five separate " tracks" to describe the competing approaches.The first track reflects approximately eleven jurisdictions that have retained joint and several liabilities even after adopting the comparative fault. The second track describes the opposite approach ( adopted by approximately 14 states ) of applying only several liability to
多虚拟防火墙——ASA Multiple Context Mode Security Context Overview 我们可以将一个单一的物理防火墙逻辑上分为多个虚拟防火墙，每个虚拟防火墙都是独立的设备，它们有自己独立的安全策略、接口和管理接口；ASA会为每个虚拟防火墙保存一个配置文件，以保存每个虚拟防火墙的这些策略和配置；这些配置文件可以保存在本地，可以保存在外部服务器上；许多特性在多虚拟防火墙模式下都被支持，包括routing table、防火墙特性、IPS以及管理，但是也有一些特性不被支持，包括VPN、组播以及动态路由协议； 系统配置 在系统配置下，我们可以创建、删除、修改以及管理虚拟防火墙。比如我们在系统配置下面创建虚拟防火墙，并且指定将哪些接口分配给哪些虚拟防火墙等，只有在系统配置下创建了虚拟防火墙后，我们才可以使用changeto命令到单个虚拟防火墙下对其进行配置，虚拟防火墙的配置和物理防火墙的配置方法一样，只不过不支持动态路由协议、组播以及VPN；系统配置中除了包含有指定的Failover接口外，不包含任何接口； Admin Context Admin context 和其他虚拟防火墙一样，只是用户登录到该虚拟防火墙上以后，就拥有了系统管理员的权限，并能够访问系统以及其他虚拟防火墙；虚拟防火墙必须存储在Flash中，不能够存储在外部服务器上； Admin context必须先于其他的虚拟防火墙进行创建和配置，如果系统已经在多模式或从single模式切换到多模式下的时候，admin context会在Flash中自动创建一个admin.cfg文件。如果不想使用admin.cfg作为admin context，你可以使用admin-context命令改变； 2. 流量分类 将一个物理防火墙划分为多个虚拟的防火墙，那么就要涉及到一个对流量分类的问题，物理防火墙收到一个流量/连接后，要能够区分出该连接是属于哪个虚拟防火墙的；ASA对流量的分类有三中方法： 将一个接口唯一的划分到一个虚拟防火墙中（基于接口） 如果一个接口唯一的只属于一个虚拟防火墙，那么所有从这个接口进入的流量都应该只属于这一个防火墙。在透明模式下，一个接口唯一属于一个虚拟防火墙是必须的； 基于MAC地址 多虚拟防火墙中，如果一个接口同时属于多个虚拟防火墙（即共享接口），那么可以使用基于MAC地址来对流量进行分类。这时候就要求在每个虚拟防火墙上，为该共享接口指定一个不同的MAC地址，该MAC地址可以自动产生，也可以手工指定； 基于NAT 除了基于MAC地址分类以外，还可以基于NA T来对流量进行分类；分类器会截取流量，并执行一个目的地址查找，所有其他域都被忽略，只有目的地址被使用。为了使用目的地址对流量进行分类，那么分类器就必须知道每个虚拟防火墙后面的子网。分类器根据NA T的配置，来决定每个虚拟防火墙后面的子网；分类器将目的地址和static命令或global做匹配；以下的方法不被用于包的分类： NA T免除 路由表——如果一个虚拟防火墙包含一个到达一个子网（A）的指向外部路由器的静态路由，并且同时包含一个关于子网A的static路由，那么分类器使用static命令来对包进行分类； 以下是包分类的例子：
多元（複）迴歸分析 （Multiple Regression Analysis） 1、利用OLS（ordinary least squares）來做多元迴歸可能是社會學研究中最常用 的統計分析方法。利用此法的基本條件是應變項為一個分數型的變項（等距尺度測量的變項），而自變項之測量尺度則無特別的限制。當自變項為類別變項時，我們可依類別數（k）建構k-1個數值為0與1之虛擬變項（dummy variable）來代表不同之類別。因此，如果能適當的使用的話，多元迴歸分析是一相當有力的工具。 2、多元迴歸分析主要有三個步驟： ─第一、利用單變項和雙變項分析來檢視各個準備納入複迴歸分析的變項是否符合OLS線性迴歸分析的基本假定。 ─選定迴歸模式，並評估所得到的參數估計和適合度檢定（goodness of fit）。 ─在我們認真考慮所得到的迴歸分析結果前，應做殘餘值（residuals）之診斷分析（diagnosis）。但通常我們是先確定迴歸模式之設定（specification）是否恰當後，才會做深入之殘餘值分析。 3、迴歸分析的第一步是一一檢視每個即將納入迴歸分析模式的變項。首先，我 們必須先確定應變項有足夠的變異（variability），而且是接近常態分配（迴歸係數的估計並不要求應變項是常態分配，但對此估計做假設測定時，則是要求殘餘值應為常態分配。而應變項離開常態分配的狀態很遠時，殘餘值不是常態分配的可能性增大）。其次，各自變項也應該有適當的變異，並且要瞭解其分配之形狀和異常的個案（outlying cases；outliers）。 我們可用直方圖（histogram）和Normal P-P（probability plot）圖等來測定應變項是否拒絕其為常態分配的假設，以及是否有異常之個案。同樣的，我們可用直方圖和其他單變項之統計來檢視各個自變項之分配形狀、程度，以及異常個案等。 在SPSS中，我們可用Analyze內的Descriptive Statistics中的Explore來得到上述之統計和圖。 4、做雙變項相關之分析之主要目的是檢視變項間之關係是否為線性關係 （linearity）和是否為共線性（collinearity）之情況。最基本的作法是看雙變項之相關矩陣。如果應變項與自變項間之關係很弱或比自變項間之相關弱的話，就應質疑所設定之多元迴歸模式是否適當。 檢視自變項與應變項間是否為線性關係的基本作法是看雙變項間之散佈圖（scatter plot）。進階且比較好的作法是在控制其他自變項後，再看某一自變項與應變項間之部分線性關係（partial linearity）。線性關係是迴歸分析重要
实习四：多序列比对（Multiple alignment） 学号姓名专业年级 实验时间提交报告时间 实验目的： 1. 学会利用MegAlign进行多条序列比对 2. 学会使用ClustalX、MUSCLE 和T-COFFEE进行多条序列比对分析 3. 学会使用HMMER进行HMM模型构建，数据库搜索和序列比对 实验内容： 多序列比对是将多条序列同时比对，使尽可能多的相同（或相似）字符出现在同一列中。多序列比对的目标是发现多条序列的共性。如果说序列两两比对主要用于建立两条序列的同源关系，从而推测它们的结构和功能，那么，同时比对多条序列对于研究分子结构、功能及进化关系更为有用。例如，某些在生物学上有重要意义的相似区域只能通过将多个序列同时比对才能识别。只有在多序列比之后，才能发现与结构域或功能相关的保守序列片段，而两两序列比对是无法满足这样的要求的。多序列比对对于系统发育分析、蛋白质家族成员鉴定、蛋白质结构预测、保守模块的搜寻以及PCR引物设计等具有非常重要的作用。 作业： 1.Align the orthologous nucleotide and protein sequences from 5 organisms you found from first practice with MegAlign. Describe the sequences you used (the title of each sequence), explain whether the phylogenetic tree is consistent with the species tree from NCBI taxonomy database. Set the alignment report to show consensus strength and decorate the residues different from consensus with green shade. (Hint: use the taxonomy common tree from NCBI to get the evolutionary relationship among the organisms. Save your organism name in a text file with each organism name in a line, and upload the file, choose Add from file, and you will see the relationship among the specified organisms) https://www.doczj.com/doc/b717751522.html,/Taxonomy/CommonTree/wwwcmt.cgi Hint 2：Change the accession number in your fasta or genPept format sequence file to organism name, so that the phylogenetic tree can be easily understood. 方法与结果： 打开Megalign，选择FILE下的Enter sequence ，打开之前保存的来自于五个物种的蛋白(或核酸)序列； 首先选择打分矩阵，点击“Align”，选择Set residue Weight Table 选择矩阵：PAM100(核酸则设为weighted)，通过“method parameters”查看参数，使用Clustal V的默认值； 其次进行序列的比对，选择Align下的“by Clustal V Method”开始比对， 再次待其结束后，进行比对结果的显示，选择view下的“Phylogenetic Tree”，显示出树形图；（图）与NCBI上找到的树形图进行对比（图）；
Multiple 1 1. The keyboard, mouse, monitor, and system unit are: hardware output devices storage devices software 2. Programs that coordinate computer resources, provide an interface, and run applications are known as: application programs operating systems storage systems utility programs 3. A browser is an example of a: basic application specialized program system application utility program 4. Although not as powerful as a supercomputer, this type of computer is capable of great processing speeds and data storage. mainframe media center midrange netbook 5. The smallest type of microcomputer: netbook handheld midrange tablet PC 6. RAM is a type of: computer memory network secondary storage 7. Unlike memory, this type of storage holds data and programs even after electrical power to the computer system has been turned off. primary RAM ROM secondary 8. The type of file created by word processors to save, for example, memos, term papers, and letters. database document presentation worksheet 9. The change in connectivity that uses the Internet and the Web to shift many computer activities from a user’s computer to computers on the Internet. cloud computing high definition network USB