On The Effect of Prior Knowledge & Academic Emotion While Learning
In this article I will analyse the correlation with and affect of a student’s prior knowledge on a given concept as well as a student’s emotional state and reaction to learning that material as a means to better understand the issues each has in a classroom style learning environment — in terms of both benefits and tradeoffs. I will first discuss the definitions of prior knowledge and a student’s’ emotions in the context of this course, and then outline the major obstacles each can inflict as well as the resources each can provide for learning. I will also discuss the pedagogical implications that both these obstacles and resources play in a classroom dynamic, drawing from papers I’ve analyzed in my courses at UC Berkeley as well as my research work on identifying students’ misunderstandings of Computer Science concepts and personal experiences in tutoring two lower division courses.
The constructivist model of learning fundamentally is the iterative process of adding new knowledge to existing ideas, concepts, emotions, observations, or beliefs about oneself and one’s environment. As such, the role of prior (or existing) knowledge becomes a critical one. All existing knowledge is held together in a (mostly) connected set of mental models that aid in contextualizing the prior knowledge such that when new concepts are acquired, a fit can be identified for them. Simply put, prior knowledge can be defined as the existent “skills, beliefs, and concepts” (Bransford et al 10) in their mental representations that students interface with when they learn more information about their world. Student’s come to a formal education system with a myriad of existing prior knowledge, ranging from beliefs about themselves and their environment to acquired skills. As a consequent of containing all of a student’s knowledge, this also means that fallacious, misplaced, or biased prior knowledge can significantly influence what “they [the student] notice about the environment and how they organize and interpret it” (Bransford et al 10).
The idea of Academic Emotions borrows from studies of test anxiety, but instead of just focusing on the student’s relative disposition towards a concept while they are under pressure to perform (such as during an assessment) it also studies their routine disposition towards the material. Accordingly this creates a dichotomy of a positive vs. negative range of emotional moods. The student can have “positive activating emotions (such as enjoyment of learning, hope for success, or pride); positive deactivating emotions (e.g., relief, relaxation after success, contentment); negative activating emotions (such as anger, anxiety, and shame); and negative deactivating emotions (e.g., boredom, hopelessness)” (Pekrun et al 97). Each one of these moods has a significant impact on the student’s performance when learning, and it will likely come as no surprise that the motivation — which is orienteered by these emotions — of a student towards a subject is directly correlated with their learning of that subject.
In conjunction with the notion of conceptual change — the gradual evolution of concepts and mental models — prior knowledge serves as a duality. Firstly, as a student seeks to build upon their existing repository of knowledge, that prior knowledge then becomes a metric to verify the accuracy of any new information, acting as a filter, raising flags for recognized inconsistencies or misfits into the model. However if the prior knowledge is fallacious or biased, then it can cause the student to incorrectly adapt new knowledge which can stifle their learning development.
Since prior knowledge acts as a home for new knowledge, placing it in accordance with its existing concepts this allows for a rich contextualizing of new information. A learner can beget nuanced creative interpretations of the idea (a novel application) and an expanded horizon of transfer opportunities as the new concepts mingles and attaches with previously recognized ideas. As a concept is developed, adding more metadata and more relationships with other concepts, complex models related to one concept can mix with other complex models providing an intrinsic understanding of the interconnectedness of the learner’s knowledge. For example, an athlete who may have experientially studied, and thus constructed a mental model of the physiology of their body and the dynamic motion of the balls they play with, may find that the targeted study and development of the mental models related to the anatomy of muscles, physiology of the cardiovascular system, geometry, and/or Newton’s Laws can allow for a richer intuition to be built upon their current mental frameworks when learning these new ideas, and can recognize and transfer their preexisting knowledge of their experiences into personalized correlations and examples.
The complexity of prior knowledge can determine the specific elements of information that a person incorporates into their model. Gentner’s study of determining relationships between images showed that adults, who had a more constructed model of items that included their function where able to match elements based on their function roles, compared with children who matched on visual similarity. An cordless drill may look quite similar to a handgun, however, functionally they offer very different purposes. This also opens the likelihood for falsely biasing new concepts. The fable of Fish Is Fish (Lionni, 1970) provides an colorful illustration of how this prior knowledge bias can affect newly adapted concepts. For the fish, upon hearing from the frog about the life outside the water, contextualizes the new information within it’s existing mental models, falsely adapting them to the constrained information it possesses “people are imagined to be fish who walk on their tailfins, birds are fish with wings, cows are fish with udders…” (Bransford & Brown 11). Harrison & Treagust also illustrate this counter effective bias in the usage of analogies, where in their study of student’s views of atoms and molecules was both properly and improperly based on the analogous models they learnt. While structural transfer (the relationship of atoms in a molecule) was made correctly by the students, the students misincorporated into their model that the electron is entirely analogous to the concept of a shell including the idea that the shell is coating of protection like an eggshell (Harrison & Treagust 523) and the atoms likely felt swishy like the pompoms in their model, rather than recognizing these models for the loose analogy that they are meant to be.
Consequently, it becomes the responsibility of the instructor to be explicit about the mapping from an analogy to the actual concept. Additionally as Bransford & Brown suggest, “learning is enhanced when teachers pay attention to the knowledge and beliefs that learners have….[and] use this knowledge as a starting point for new instruction, and monitor students’ changing conceptions… (Bransford & Brown 11) One of the research experiments I worked on focuses on identifying (tagging) particular misunderstandings a student may have about given concepts in their Introduction to Programming class, allowing instructors to determine the efficacy of an assessment question (based on the “taggability” of the question). Given the structure of the class, a system can automatically analyze a student’s knowledge base through the variation in answers from the student, allowing us to intervene automatically when the student consistently displays a misunderstanding, and offer targeted feedback that may help to alleviate it.
A student’s familiarity with a subject also plays into their academic achievement through their academic emotions. This, in turn, affects their abilities to reason about and incorporate new information. “College students’ ratings of their interest in their course material were positively related to their self-reported use of elaboration strategies, the seeking of information, and their engagement in critical thinking” (Pintrich et al 183) and to reach the level of novice familiarity characterized by self exploration, requires a degree of pre-existing knowledge. The process behaves cyclically; the more engaged a student is that is, the more information is “interesting, important, and useful to them [college students]…[the] more likely [they are] to use deeper processing strategies like elaboration and metacognitive control strategies. (Pintrich et al 183), to more effectively learn and incorporate information into more substantive model.
Being a tutor for lower division freshman year courses helped me to better understand the influence of emotion disposition to material. Firstly students have quite varied definitions of where they lie on the spectrum of ignorance and expertise, the more they underplayed their placement, the more the ideas of the imposter syndrome and a helpless disposition took place. The mere realization of their distance from purported novice hood or expertise, diminished their self worth (Wigfield et al), which in an academically taxing environment like Berkeley would either motivate them to learn with a stronger desire or leave them to wallow in a pit of self-doubt. Assumably, “positive activating emotions such as enjoyment of learning may generally enhance academic motivation, whereas negative deactivating emotions may just be detrimental (e.g., hopelessness, boredom)” (Pekrun et al 97). However the student emotions acted as a double edged sword, causing some of my tutees to be disinterested in material that seemed a step or two past their current knowledge base, or to carelessly tirade through the material with little regard for the intrinsic motivation and relationships nuanced in the material, only to burn out later. Other student’s behavior correlated more with positive emotions, with confidence in their understanding of the material, but often falsely conflated concepts together, leading to a disinterested view towards the material as “busywork”, or a disregard for sloppy mistakes “as not worth recognizing”. Others of the more positively engaged tutees were quick to recognize and fix their faulty models of understanding which was anecdotally verified to be result of confidence that emerged from previous encounters with similar situations.
This of course may all sound like a series of platitudes; of course you won’t engage as much with something you aren’t interested in compared to something you are. But part of the learning process for both students and teachers is understanding the delicate dance between prior knowledge and student emotion, and how gray the line is between motivation and disengagement. Both positive and negative emotions may both equally produce a drive to learn or induce disconnection, and prior knowledge can be both incredibly advantageous in the cases of properly contextualizing information and in transference, but can be equal parts dangerous by producing fallacious overconfidence in students, or misguiding the students through false analogy or incorrect connections.
From a pedagogical standpoint, these challenges imply that actions on the student and teachers part should be taken to avoid them. Many of the papers throughout this course offer suggestions, such as diSessa who attests to the development in conceptual change research that, rather than criticise the subject matter for being too abstract or complex or the instructor for not “simplifying exposition” or “saying it better”, one should “instead of rejecting student (mis)conceptions altogether….pick and choose the most productive ideas, and refine them to create normative concepts. Additionally, “Listening to students can enhance science teaching if teachers take the time to carefully consider the mental models that the students either bring to instruction or construct during instruction.” (Harrison & Treagust 532) Furthermore enthusiastically teaching, focusing on creative competitions, and providing incisive feedback on progress and achievement helps to foster emotions that lean towards enjoyment of learning and hope for success (Pekrun et al). And lastly the onus of veridical learning can be also be put on the student, as Bransford & Schwartz states, the “[Preparation for Future Learning] perspective….prepare(s) people to resist making old responses by simply assimilating new information to their existing concepts or schemas…[and to instead] look critically at their current knowledge and beliefs as a whole. (Bransford & Schwartz 80). In this way by making the effort to analyze a student’s mental models, with their actualities and inaccuracies, surgically targeting and correcting those misunderstandings, and encouraging students to engage in a passionate and critical disposition to material, we can help facilitate more effective learners.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). From speculation to science. Pp. 3–27 in: How people learn: Brain, Mind, Experience, and School, Expanded Edition. Washington DC: National Academies Press. Chapter 1.
Bransford, J.D., & Schwartz, D.L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24, 61–100.
diSessa, A. A. (in press). A History of Conceptual Change Research: Threads and Fault Lines. Cambridge University Press. 2nd edn.
Gentner, D. (2010). Bootstrapping the mind: Analogical processes and symbol systems. Cognitive Science, 34(5), 752–775.
Harrison, A. G., & Treagust, D. F. (1996). Secondary students’ mental models of atoms and molecules: Implications for teaching chemistry. Science education, 80(5), 509–534.
Kristin Stephens-Martinez, An Ju, Colin Schoen, John DeNero, and Armando Fox. 2016. Identifying Student Misunderstandings using Constructed Responses. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (L@S ‘16). ACM, New York, NY, USA, 153–156. DOI: http://dx.doi.org/10.1145/2876034.2893395
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37(2), 91–105.
Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63, 167–199.
Sandoval, W. A. (2005). Understanding students’ practical epistemologies and their influence on learning through inquiry. Science Education, 89(5), 634–656.
Wigfield, A., Eccles, J.S., Schiefele, U., Roeser, R.W., & Kean, P.D. (2006). Development of achievement motivation. In W. Damon & R.M. Lerner (Series Eds.) & N. Eisenberg (Volume Ed.), Handbook of Child Psychology, 6th Edition, Vol. 3, Social, Emotional and Personality Development (6th ed., Vol. 3, pp. 933–1002). New York: Wiley.