This video presents the concept of changing the interface of h... This video presents the concept of changing the interface of higher education. With the development of new technological interfaces there is need for the development of a new pedagogical framework. Ref. Sonwalkar, N. "Changing the Interface of Higher Education," iUniverse, NY, Chapter 1

In this video, Dr. Nish Sonwalkar (Sc.D., MIT) provides a framework for online education that combines multimedia, learning models, interactivity including social media interaction. The model represents the "Learning Cube" framework that provides dimensions of media, models and interactivity as a cubical learning space where one can create individualized learning experience with adaptive learning. Ref. Sonwalkar, N. "Changing the Interface of Higher Education," iUniverse, NY, Chapter 2

In this video, Dr. Nish Sonwalkar (Sc.D., MIT) describes the d... In this video, Dr. Nish Sonwalkar (Sc.D., MIT) describes the development of adaptive pathways for learning by creating five different instructional strategies to accommodate individual preferences.

Ref. Sonwalkar, N. "Changing the Interface of Higher Education," iUniverse, 2004, Chapter 3

In this video Dr. Nish Sonwalkar explains differentiated instructions that can be used to encourage five pedagogical models to enhance learning performance. The learning cube framework allows creation of course structures that confirm to propose learning models allows creation of courses with multiple learning styles. Ref. Sonwalkar, N."Changing the Interface of Higher Education," iUniverse, NY, Chapter 4.

In this video, Dr. Nish Sonwalkar (SC.D., MIT) presents the implementation of adaptive learning using five different learning models and collecting statistics on learning performance based on the diagnostic assessments. The system that adapts to individual learning pathway and provides feedback to improve the learning performance.

Ref. Sonwalkar, N."Changing the Interface of Higher Education," iUniverse, NY, Chapter 5.

In this video, Dr. Nish Sonwalkar (Sc.D., MIT) presents the methods of evaluation and assessments for online courses and programs. The evaluation of online course requires a multi-dimensional approach. Dr. Sonwalkar presents a framework of pedagogical effectiveness index, which incorporates the dimensions of multi-media, learning models and interactivity to assess and rate effectiveness of online courses.

Ref. Sonwalkar, N. "Changing the Interface of Higher Education," iUniverse, NY Chapter 6

In this Video, Dr. Nish Sonwalkar (Sc.D., MIT) presents relationship between personality type and learning strategies to optimize individual learning performance.

Dr. Nish Sonwalkar in this video presents his view on why the current online learning systems lead to low completion rates. The use of personalized adaptive learning systems can lead to higher completion by facilitating different learning strategies for motivation, real-time feedback to keep students engaged and opportunity to improve with each learning cycle. The adaptive learning systems have been observed to provide very high retention and degree completion rates.

In this video Dr. Nish Sonwalkar (Sc.D., MIT) is presenting recent developments in Mobile Learning. This presentation was made for an event hosted at face-book for the large number of friends and fans of Dr. Sonwalkar. He is pioneer in the field of Adaptive Learning and has now created technology called Adaptive Mobile Learning (AMOL).

Dr. Sonwalkar describes four components of educational adaptive learning systems. The four components are - pedagogical framework with distinct learning strategies, learning trajectory, evolution of effectiveness of learning trajectory and intelligent revision for reaching desired learning outcome. These are four essential components that are necessary to create any adaptive and/or personalized learning systems for the online education.

The brain-based adaptive learning depends on creation of cognitive learning strategies to present content. In this video Dr. Nish Sonwalkar (Sc.D., MIT) takes you through five distinct learning strategies that are used in the adaptive learning systems. The five strategies are -- apprentice (step-by-step learning), incidental (case-based), inductive (example based), deductive (interactive learning by doing), discovery (learning by inquiry in a virtual environment). The content presented in cognitive learning strategies is then used to personalize the learning experience in an adaptive learning environment.

In this video, Dr. Nish Sonwalkar points out the surge in the smart phones and the apps that are flooding the market spaces -- such as, App Store, iTunes, App Market. The iPad sales alone reached 40 Million in North America during the year 2011.

The adaptive mobile learning will be different in its nature on these mobile devices like iPhone, iPad, Android Phone, Tablets as there is a paradigm shift caused by the integration of educational resources.

The thousands of apps that are created will now be integrated together to create your own personal learning profile and this integration will lead to creation of an adaptive mobile learning that will be highly personalized experience.

IDEAS Boston 2013: Nish Sonwalkar - Academic, Scientist, and Musician: Dr. Sonwalkar presented "Changing the Interface of Education with Brain-wave Adaptive Learning," at the Ideas Boston invited presentations. He was introduced and interviewed by Tom Ashbrook, host of NPR program "On point.". The Ideas Boston is event organized by University of Massachusetts, Boston. Republished with permission from UMB at sonwalkar7 channel.

In this keynote presentation, Dr. Nish Sonwalkar, makes an urgent call for action to offer Brain-based Adaptive Learning to provide individualized learning for students with disabilities. The keynote address was delivered at the one day symposium organized by Liberated Learning Consortium and Ross Center of Disability at the University of Massachusetts, Boston.

The digital revolution with the development of World Wide Web and Mobile systems has given rise to ubiquitous access of information to both the digital immigrant and digital native population. However, the information overload has caused confusion on how best to utilize digital revolution for the benefit of learning in general and distance learning in particular. On the other side of the equation, noninvasive techniques for the study of neurophysiology of brain, such as, PET, fMRI, and CT-scans have provided a wealth of information on the activities of neurons that lead to the formation of learning networks in the human brain. The combination of digital technology with the new frameworks of brain-based adaptive learning and brain-machine interface are the new frontiers for enabling high performance, efficient, effective and engaging distance education.

Dr. Sonwalkar, an authority in the field of brain-based learning will provide an overview of the theory of adaptive learning and propose a pedagogical framework to assimilate the new meaningful liberated distance learning paradigm.

This is the video recording of keynote speech delivered by Dr. Nish Sonwalkar (Sc.D., MIT) at the Research :Leaders Conference organized by NSU on July 20, 2011.

The creative thinking is often considered a realm of poets, singers, artists, designers and mathematician, such as, Einstein, Picasso, Stravinsky and Gandhi. Recent neurological research shows that creativity is an acquired skill that can be developed by a systematic divergent learning approach. The creative thinking is an essential and often necessary aspect of successful graduate research.

The investigations conducted by brain and cognitive science using the advanced neurological mapping methods, such as, Electroencephalogram (EEG), functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET) provide a wealth of new information to shed light on the process that helps us learn about creative thinking. Recent studies indicate that the creativity is not confined to right brain regions and is dispersed on both left and right side of the brain and is strongly connected with the emotional centers of the human brain.

In this video segment Dr. Nish Sonwalkar (Sc.D., MIT) explains the difference between intelligent tutors and the adaptive learning systems.

The intelligent tutors were created in early 1980, at the time when artificial intelligence was in fashion. The AI was based on rule based systems where certain rules were created based on the expert knowledge to provide feedback in real time. So the tutors normally were very domain specific.

For example math tutors where fed with a knowledge representation for the domain of math with expert rules that were fired when a learner was doing a mathematical task. Based on the answers of a given task subsequent scenario were presented. Each scenario steps led to certain rules that provided led to inference by the intelligent tutor to provide certain feedback or practice test.

By repeated task and feedback intelligent tutors were able to provide reasonable response to each mistake made by the used in their understanding of the mathematical concept. While tutors worked well for a specific domain that was deterministic in nature they failed in areas where there were no correct deterministic answers. Because, there were no if-then-else rules that you could fire in an area like interpretation of poetry.

The Adaptive Learning Systems on the other had represent knowledge in distinct cognitive pathways such as apprentice, incidental, inductive deductive and discovery -- providing same content in a different contextual framework thereby providing significant cognitive opportunity to learn. Also in the education adaptive learning systems the feedback is given in a different learning pathway to provide another perspective on the same problem.

Therefore, the an authentic adaptive learning systems is not domain specific and can provide better learning for both scientific disciplines with deterministic answers to domains where the answers are more based interpretation style and there no correct answer.

In this video segment Dr. Nish Sonwalkar (Sc.D., MIT) provides difference between Adaptive Testing and Adaptive Learning.

Most often the adaptive learning is confused with the adaptive testing. The adaptive testing is a method in which the difficulty level of questions is adjusted continuously to ascertain the competency level of a learner.

The questions are posed to the learner, on a given subject matter, with known level of difficulty, say medium level of difficulty and if the student answers questions correctly then the difficulty level is increased. The level of difficulty is increased in each questions posed until the learner starts not answering questions. Then the difficulty level is reduced. By going over several related subject matter areas after sufficient number of questions/answer sessions a score is calculated for the student and then he is put on a percentile basis on the scale by the adaptive testing agency who already have a corpus of normalized data with percentile distribution.

Most of the testing agencies for aptitude testing use adaptive testing, such as, GRE, GMAT, SAT etc. In adaptive testing based adaptive learning systems the content level of difficulty and explanation is varied based on the question/answer set. However in such systems there is no distinct pedagogy that is used for the variation of content presentation to the user for cognitive opportunity enhancement.

In true, adaptive learning system, first requirement is to have a pedagogical framework for the presentation of content in three or more learning paths and then there is assessment engine that will provide continuous feedback and rotation of learners through the learning path to reach a desired competency level with desired learning outcome.

Hence most of the claims made by the adaptive learning systems that are purely based on adaptive testing do not provide sufficient distinction of content in terms of cognitive learning strategies to be termed as adaptive learning systems. We need to dispel the myth that adaptive testing based content presentation systems are adaptive systems and do not match with the learning preference of the individual learners.