“Few books risk such damage to the public understanding of science as those by Oliver James. Inexplicably popular despite their scientific illiteracy and mediocre writing, they are promoted widely by James’s regular, shriekingly aggressive media appearances. A glance at the studies shows the absurdity of the extreme blank-slate position advanced in Not In Your Genes: environments clearly matter, but so does DNA, and the perversity of denying this becomes ever more acute with each new genetic discovery. Truly understanding human psychology and helping those with psychiatric illnesses requires us to have a realistic view of the causes of differences between people. That realistic view is Not In This Book.” (excerpt)
Archive for the ‘intelligence’ Category
Tags: DNA, human psychology, myths, nature vs. nurture, psychiatric illness, psychology, science, scientific illiteracy, scientific literacy, studies
Tags: confidence, Dunning-Kruger effect, high sensitivity, ignorance, impostor syndrome, success
“My experience it’s not natural for men to admit feelings of discomfort and vulnerability. So you have to dig deeper and work a lot harder to get under their skin,”
Tags: case, common sense, declarative knowledge, deduct, deduction, deductive, deep knowledge, episodic knowledge, explicit knowledge, formal reasoning, human reason, inductive reasoning, know-how, knowledge, lessons, practical experience, procedural knowledge, reasoning by analogy, scientific discovery, semantic knowledge, shallow knowledge, tacit knowledge
- Deep Knowledge: Knowledge acquired through years of proper experience.
- Shallow Knowledge: Minimal understanding of the problem area.
- Knowledge as Know-How: Accumulated lessons of practical experience.
- Reasoning and Heuristics: Some of the ways in which humans reason are as follows:
- Reasoning by analogy: This indicates relating one concept to another.
- Formal Reasoning: This indicates reasoning by using deductive (exact) or inductive reasoning.
- Deduction uses major and minor premises.
- In case of deductive reasoning, new knowledge is generated by using previously specified knowledge.
- Inductive reasoning implies reasoning from a set of facts to a general conclusion.
- Inductive reasoning is the basis of scientific discovery.
- A case is knowledge associated with an operational level.
- Common Sense: This implies a type of knowledge that almost every human being possess in varying forms/amounts.
- We can also classify knowledge on the basis of whether it is procedural, declarative, semantic, or episodic.
- Procedural knowledge represents the understanding of how to carry out a specific procedure.
- Declarative knowledge is routine knowledge about which the expert is conscious. It is shallow knowledge that can be readily recalled since it consists of simple and uncomplicated information. This type of knowledge often resides in short-term memory.
- Semantic knowledge is highly organized, “chunked” knowledge that resides mainly in long-term memory. Semantic knowledge can include major concepts, vocabulary, facts, and relationships.
- Episodic knowledge represents the knowledge based on episodes (experimental information). Each episode is usually “chunked” in long-term memory.
- Another way of classifying knowledge is to find whether it is tacit or explicit
- Tacit knowledge usually gets embedded in human mind through experience.
- Explicit knowledge is that which is codified and digitized in documents, books, reports, spreadsheets, memos etc.
via Kinds of Knowledge.
Tags: data, information, knowledge, wisdom
- Data represents unorganized and unprocessed facts.
- Usually data is static in nature.
- It can represent a set of discrete facts about events.
- Data is a prerequisite to information.
- An organization sometimes has to decide on the nature and volume of data that is required for creating the necessary information.
- Information can be considered as an aggregation of data (processed data) which makes decision making easier.
- Information has usually got some meaning and purpose.
- By knowledge we mean human understanding of a subject matter that has been acquired through proper study and experience.
- Knowledge is usually based on learning, thinking, and proper understanding of the problem area.
- Knowledge is not information and information is not data.
- Knowledge is derived from information in the same way information is derived from data.
- We can view it as an understanding of information based on its perceived importance or relevance to a problem area.
- It can be considered as the integration of human perceptive processes that helps them to draw meaningful conclusions.
Tags: accumulation of facts, common sense, experience, fact, heuristic, knowledge, learning, memory, procedural rule, understanding
- Knowledge can be defined as the “understanding obtained through the process of experience or appropriate study.”
- Knowledge can also be an accumulation of facts, procedural rules, or heuristics.
- A fact is generally a statement representing truth about a subject matter or domain.
- A procedural rule is a rule that describes a sequence of actions.
- A heuristic is a rule of thumb based on years of experience.
- Intelligence implies the capability to acquire and apply appropriate knowledge.
- Memory indicates the ability to store and retrieve relevant experience according to will.
- Learning represents the skill of acquiring knowledge using the method of instruction/study.
- Experience relates to the understanding that we develop through our past actions.
- Knowledge can develop over time through successful experience, and experience can lead to expertise.
- Common sense refers to the natural and mostly unreflective opinions of humans.
IQ’s Corner: The Motivation and Academic Competence MACM Commitment Pathway to Learning Model: Crossing the Rubicon to Learning ActionPosted: November 2, 2014 in Awesome Living, Cognitive Science and Education Research, College and Career Readiness, Education, education innovation, Insight, intelligence, Knowledge, psychology, Resilience, Thinking
Tags: academic competence, conative domain, Haertel, learning, McGrew, motivation, PiPerna, school psychology, Snow, Walberg, Wang
“A number of comprehensive models of school learning have been advanced to describe and explain the school learning process (see McGrew, Johnson, Cosio, & Evans, 2004). Walberg’s (1981) theory of educational productivity is one of the few empirically tested theories of school learning. Walberg’s model is based on an extensive review and integration of over 3,000 studies (DiPerna, Volpe & Stephen, 2002; Wang, Haertel, and Walberg, 1997). Walberg et al. reported that the following key variables are important for understanding school learning—student ability and prior achievement, motivation, age or developmental level, quantity of instruction, quality of instruction, classroom climate, home environment, peer group, and exposure to mass media outside of school (Walberg, Fraser & Welch, 1986). The first three variables (ability, motivation, and age) reflect student individual difference characteristics. The fourth and fifth variables reflect characteristics of instruction (quantity and quality), and the final four variables (classroom climate, home environment, peer group, and exposure to media) represent aspects of the psychological environment(DiPerna et al., 2002). Clearly student characteristics are important for school learning, but they only comprise a portion of the complete learning equation.
“The Walberg research group (see Wang, Haertel, & Walberg, 1993) also concluded that psychological, instructional, and home environment characteristics (proximal variables) had a more significant impact on achievement than variables such as state-, district-, or school-level policy and demographics (distal variables). More important for practicing school psychologists was the conclusion that student characteristics (i.e., social, behavioral, motivational, affective, cognitive, metacognitive) were the set of proximal variables that had the most significant impact on learner outcomes (DiPerna et al., 2002).”
Tags: agentic theory, Bandura, connections, human agency, resilience, self-efficacy, video
Albert Bandura, the David Starr Jordan Professor (Emeritus) of Social Science in Psychology at Stanford University and winner of the 2012 IUPsyS Lifetime Career Award, presents his talk entitled, “Toward an Agentic Theory for the New Millennium”.
The takeaways from this video: research topics like “human agency”, “agentic theory”, self-efficacy, resilience, “social cognitive theory”.
Following these lines of research, actually reading the articles for yourself, you begin to realize that there was already a great body of work and research before “grit”. You can listen carefully to the video, list the quotes Bandura cites, and track down some of the terms and concepts in Wikipedia and then in the actual articles he has written or co-authored. Distinctions between Duckworth’s Grit and Bandura’s exploration of self-efficacy’s contributions to agentic theory model (social cognitive theory) are artificial. It kills me that so many articles include “grit” in their titles without awareness that there is a great body of work developed by Albert Bandura.
Tags: dropout, Dropout Prevention, education, explicit instruction, resilience, self-efficacy, wraparound services
To help solve the problem, the report says, administrators and schools should:
my response to the article: “More detailed suggestions: Explicit instruction and mentoring or coaching of leadership skills (not just throwing students in to the “deep end”. Wraparound services in the community (not just identification and referrals for outside services. Use measures of resilience skills and experiences to identify at-risk students in addition to the negative experiences that undermine perceived self-efficacy or stigmatizes students in ways to reject them from positive experiences). “