In order to resolve the cognitive conflict, the deep learner makes meaningful, nonarbitrary, and substantive connections between the input story and another story that the input story activates from the learner’s own cognitive structure, which is referred to as the existing story or the activated existing story. A motivating input story makes the learner search through his or her repertoire of existing stories, which are indexed in the learner’s brain according to convenient labels, and select the one that best links to the input story5. In biological terms, a learner receives input from the outside world through the brain’s sensory cortex. This input is transmitted to the back integrative cortex, which integrates sensory information to create images and meaning. Then, the frontal integrative cortex analyzes these images, solves the problem, and comes up with a solution (Zull, 2002). As a way of illustration, suppose that in a criminal justice class you ask your stirdents the following question: “Do you think that the main role of the prosecutor in a criminal trial should be to seek a conviction or to arrive at the truth?’’ This question may activate different stories in students’ minds. Some may connect it to a popular culture show that they indexed in their minds as “prosecutor and convictions”. Others may connect it to a particular aspect of a reading they did for the class. Yet, other students may connect it to a TV show that they indexed as “criminal

’From the information received from the input story, the learner selects only what is relevant for the activated, existing knowledge, and discards the irrelevant. Then, from this selected information, the learner makes abstractions and generalizes its meaning (Carretero, 2009).

trial”. Some other students may have had a personal experience with a prosecutor in a criminal trial and may connect the question hi the input story to that experience. The richer their readings, experiences, and prior knowledge, the more meaningful the connections will be.

In the deep learning process, the learner makes this connection between the new knowledge arising from the input stoiy and the existing story by engaging in a series of higher order cognitive skills, competences, and processes. These skills, processes, and competences include critical analysis, synthesis, problem solving, extrapolation, theorization, comparison, contrast, evaluation, and rapid cognition, among others. For example, in Manhattan (Allen, 1979), Mary Wilke discusses art with Isaac. While listening to Isaac praise the Castelli photographic exhibition (input story), Mary searches through her repertoire of existing stories and selects the story of Diane Arbus photography. Mary compares Castelli’s work with that of Arbus. She can identify those aspects of Arbus' photographs that are absent in Castelli’s works. Mary critically evaluates Castelli’s works. She hypothesizes about the quality of his works and reaches a conclusion about the artistic value of Castelli’s exhibition. These are all higher order processes that are directly related to the input story and that will lead to deep learning about art and photography, provided the other aspects of the process are present. In contrast, when Ms. Horr ible Harriet Hare lectures her stirdents, they simply listen to her explanations and take down notes, which they will later memorize and reproduce back to her. The competences that students use to make connections, if any, to Ms. Hare's input story are low-order. Thus, learning is a consequence of thinking, and “knowledge comes on the coattails of thinking. As we think about and with the content that we are learning, we truly learn it” (Perkins, 2009)6.

‘There are many lists and taxonomies that help us classify these skills, competences, and processes. Bloom’s taxonomy is the most widely used set of cognitive skills. The higher levels of Bloom’s taxonomy (application, analysis, synthesis, and evaluation) may help promote deep learning (Bloom, 1984). Roger Schkank came up with the 12 cognitive processes that are considered essential for a good education. These are: conceptual processes: (1) prediction, (2) modeling, (3) experimentation, (4) evaluation, analytic processes: (5) diagnosis, (6) planning, (7) causation, (8) judgment, social processes: (9) influence, (10) teamwork, (11) negotiation, and (12) describing. Similarly, Biggs and Tang (2007) classify these competences as those that merely help increase knowledge, which they refer to as quantitative and those that help deepen understanding, which are qualitative in nature. Quantitative competences mchide identifying, doing simple procedures, enumerating, describing, listing, combining, and doing algorithms, among others. Qualitative competences include comparing, contrasting explaining causes, analyzing, relating, applying, theorizing, generalizing, hypothesizing, and reflecting. Quantitative competences usually lead to superficial learning, whereas qualitative ones may lead to deep learning (Biggs and Tang, 2007).

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