Subgoal labeling

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Subgoal labeling is giving a name to a group of steps, in a step-by-step description of a process, to explain how the group of steps achieve a related subgoal. This concept is used in the fields of cognitive science and educational psychology.

Cognitive science interdisciplinary scientific study of the mind and its processes

Cognitive science is the interdisciplinary, scientific study of the mind and its processes. It examines the nature, the tasks, and the functions of cognition. Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision to logic and planning; from neural circuitry to modular brain organization. The fundamental concept of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

Educational psychology is the branch of psychology concerned with the scientific study of human learning. The study of learning processes, from both cognitive and behavioral perspectives, allows researchers to understand individual differences in intelligence, cognitive development, affect, motivation, self-regulation, and self-concept, as well as their role in learning. The field of educational psychology relies heavily on quantitative methods, including testing and measurement, to enhance educational activities related to instructional design, classroom management, and assessment, which serve to facilitate learning processes in various educational settings across the lifespan.

Contents

Lower-level steps of a worked example are grouped into a meaningful unit and labeled. This labeling helps learners identify the structural information from incidental information. [1] Learning subgoals can reduce cognitive load when problem solving because the learner has fewer possible problem-solving steps to focus. [1] Subgoal-labeled worked examples might provide learners with mental model frameworks. In a recent study, Learners who were given labels for subgoals used those labels when explaining how they solved a problem, suggesting that's how they mentally organized the information. [1]

The worked-example effect is a learning effect predicted by cognitive load theory. Specifically, it refers to the learning effect observed when worked-examples are used as part of instruction, compared to other instructional techniques such as problem-solving and discovery learning. According to Sweller: "The worked example effect is the best known and most widely studied of the cognitive load effects".

Learning is the process of acquiring new, or modifying existing, knowledge, behaviors, skills, values, or preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in some plants. Some learning is immediate, induced by a single event, but much skill and knowledge accumulates from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.

In cognitive psychology, cognitive load refers to the effort being used in the working memory. Cognitive load theory differentiates cognitive load into three types: intrinsic, extraneous, and germane.

Introduction

Generally problem solving adopts a very procedural approach. Problem solving in the areas of science, technology, engineering and mathematics (STEM) has been highly procedural. The best approach so far is to teach these procedures through instructional text accompanied by specific worked examples. The role of instructional text is to define and describe the problem solving procedures whereas how to apply these procedures is shown through worked examples. [2] Students can learn from step-by-step approach of worked examples which later can be helpful to them in solving similar problems on their own. [3] Novices, however, often find it difficult to distinguish domain specific information and the information specific to solving that problem, which increases their cognitive load. [4] This cognitive load can be reduced by use of subgoal labeling which is achieved by grouping functionally-similar steps under a label that describes that function. This approach can be helpful to students to form a mental model of the domain related problem which later can guide them to solve different problems in that domain. [4] Understanding the structure of worked example can help students identify the similarities between different problems thus encouraging self-explanation and learning. [5]

Science, Technology, Engineering and Mathematics (STEM), previously Science, Math, Engineering and Technology (SMET), is a term used to group together these academic disciplines. This term is typically used when addressing education policy and curriculum choices in schools to improve competitiveness in science and technology development. It has implications for workforce development, national security concerns and immigration policy.

Application

Subgoal labels have been used in worked examples to teach learners to solve problems in STEM domains [2] Pairing subgoal labeled instructional text with subgoal labeled worked examples can further improve learners performance in problem solving in a computer-based learning environment (e.g. online learning) without personal interaction with an instructor. [3] [4] Subgoal labels can be used in different important areas such as teaching and learning novel problem solving, in training teachers to teach technical subjects (e.g. teaching computer programming), multi agent programming, professional development, online learning and other types of lifelong learning (e.g. Subgoal labeled instruction material helped novices to program in App Inventor for Android). [1] [3] [4] [5] [6]

App Inventor for Android web application allows newcomers to computer programming to create software applications for the Android operating system

App Inventor for Android is an open-source web application originally provided by Google, and now maintained by the Massachusetts Institute of Technology (MIT).

See also

An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

Related Research Articles

Learning theory (education) conceptual frameworks in which knowledge is absorbed, processed, and retained during learning

Learning Theory describe how students absorb, process, and retain knowledge during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a world view, is acquired or changed and knowledge and skills retained.

Instructional design (ID), also known as instructional systems design (ISD), is the practice of systematically designing, developing and delivering instructional products and experiences, both digital and physical, in a consistent and reliable fashion towards an efficient, effective, appealing, engaging and inspiring acquisition of knowledge. The process consists broadly of determining the state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models but many are based on the ADDIE model with the five phases: analysis, design, development, implementation, and evaluation.

Instructional scaffolding is the support given during the learning process which is tailored to the needs of the student with the intention of helping the student achieve his/her learning goals This learning process is designed to promote a deeper level of learning.

In psychology, cognitivism is a theoretical framework for understanding the mind that gained credence in the 1950s. The movement was a response to behaviorism, which cognitivists said neglected to explain cognition. Cognitive psychology derived its name from the Latin cognoscere, referring to knowing and information, thus cognitive psychology is an information-processing psychology derived in part from earlier traditions of the investigation of thought and problem solving.

Procedural knowledge, also known as imperative knowledge, is the knowledge exercised in the performance of some task. See below for the specific meaning of this term in cognitive psychology and intellectual property law.

A cognitive tutor is a particular kind of intelligent tutoring system that utilizes a cognitive model to provide feedback to students as they are working through problems. This feedback will immediately inform students of the correctness, or incorrectness, of their actions in the tutor interface; however, cognitive tutors also have the ability to provide context-sensitive hints and instruction to guide students towards reasonable next steps.

Situated cognition is a theory that posits that knowing is inseparable from doing by arguing that all knowledge is situated in activity bound to social, cultural and physical contexts.

Constructivism (philosophy of education) philosophical viewpoint about the nature of knowledge; theory of knowledge

Constructivism is a philosophical viewpoint about the nature of knowledge. Therefore, it represents an epistemological stance.

Problem solving consists of using generic or ad hoc methods in an orderly manner to find solutions to problems. Some of the problem-solving techniques developed and used in philosophy, artificial intelligence, computer science, engineering, mathematics, or medicine are related to mental problem-solving techniques studied in psychology.

Metacognition is "cognition about cognition", "thinking about thinking", "knowing about knowing", becoming "aware of one's awareness" and higher-order thinking skills. The term comes from the root word meta, meaning "beyond". Metacognition can take many forms; it includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two components of metacognition: (1) knowledge about cognition and (2) regulation of cognition.

Cognitive apprenticeship is a theory that emphasizes the importance of the process in which a master of a skill teaches that skill to an apprentice.

Model-centered instruction is a general theory of instructional design developed by Andrew S. Gibbons. This theory can be used to design individual and group instruction for all kinds of learning in any type of learning environment. In addition, this theory may be used to design instruction with a wide variety of technologies and many media delivery systems.

E-learning theory describes the cognitive science principles of effective multimedia learning using electronic educational technology.

The split-attention effect is a learning effect inherent within some poorly designed instructional materials. It is apparent when the same modality is used for various types of information within the same display. To learn from these materials, learners must split their attention between these materials to understand and use the materials provided.

The expertise reversal effect refers to the reversal of the effectiveness of instructional techniques on learners with differing levels of prior knowledge. The primary recommendation that stems from the expertise reversal effect is that instructional design methods need to be adjusted as learners acquire more knowledge in a specific domain. Expertise is described as "the ability to perform fluently in a specific class of tasks."

Seductive details are often used in textbooks, lectures, slideshows, and other forms of educational content to make a course more interesting or interactive. Seductive details can take the form of text, photos, illustrations, sounds or music and are by definition: (1) interesting and (2) not directed toward the learning objectives of a lesson. John Dewey, in 1913, first referred to this as “fictitious inducements to attention.” While illustrated text can enhance comprehension, illustrations that are not relevant can lead to poor learning outcomes. Since the late 1980s, many studies in the field of educational psychology have shown that the addition of seductive details results in poorer retention of information and transfer of learning. Thalheimer conducted a meta-analysis that found, overall, a negative impact for the inclusion of seductive details such as text, photos or illustrations, and sounds or music in learning content. This reduction to learning is called the seductive details effect. Recently, there have been many criticisms of this theory. Critics cite unconvincing and contradictory evidence to argue that seductive details do not always impede understanding and that seductive details can sometimes be motivating for learners.

References

  1. 1 2 3 4 Margulieux, L. E., Guzdial, M., & Catrambone, R. (September 2012). "Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications". ICER '12, Proceedings of the ninth annual international conference on International computing education research: 71–78. doi:10.1145/2361276.2361291.
  2. 1 2 Margulieux, L. E. (2014). Subgoal Labeled Instructional Text and Worked Examples in STEM Education
  3. 1 2 3 Margulieux, L. E., Catrambone, R., & Guzdial, M. Subgoal Labeled Worked Examples Improve K-12 Teacher Performance in Computer Programming Training (2013)
  4. 1 2 3 4 Catrambone, R. (1995). Aiding subgoal learning: Effects on transfer. Journal of Educational Psychology , 87(1), 5.
  5. 1 2 Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General , 127(4), 355.
  6. ↑ Chiu, C. C., & Soo, V. W. (2007). Subgoal Identification for Reinforcement Learning and Planning in Multiagent Problem Solving (pp. 37-48). Springer Berlin Heidelberg.
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