Student-Centered, Technology-Rich Learning Environments (SCenTRLE): Operationalizing Constructivist Approaches to Teaching and Learning

Abstract

This article presents a model for designing student-centered, technology-rich learning environments (SCenTRLE). The model helps educators operationalize constructivist and student-centered approaches to teaching and learning by delimiting eight instructional events for facilitating knowledge construction and the development of life-long learners. The article describes theoretical and conceptual foundations for the model, details the model and discusses enduring issues, including the use of technology, student attitudes, levels of implementation, holistic versus analytic performance assessment, and the application of constructivist principles within the context of traditional instructional systems design (ISD) models. ********** Student-centered approaches to teaching and learning stress the importance of students' past experiences, exploring individual needs and interests, promoting active participation, stimulating higher-order thinking, and encouraging life-long learning (e.g., Bonk & Cunningham, 1998; APA, 1993; CTGV, 1992; Holmes Group; 1990; Brown, Collins, & Duguid, 1989). Similarly, constructivists advocate the development of environments that embed learning in authentic contexts, present learners with multiple perspectives, encourage self awareness and responsibility for learning and use modern technologies to facilitate telecommunications and the social construction of knowledge (Wilson, 1996; Duffy, Lowyck, & Jonassen, 1993; Cunningham, Duffy, & Knuth, 1993; Knuth & Cunningham, 1993). Many accede with such pedagogical methods. The challenge lies in operationalizing studentcentered, constructivist instructional strategies with a class of 30 plus students in an educational system that is more inclined to resist rather than e mbrace change. The lack of time, training, and incentives, coupled with large class sizes and incongruence with teacher beliefs, student expectations, and administrative directives appear to be some of the more pervasive reasons why classroom instruction remains predominately teacher-directed. For educators with little time, scant resources and limited exposure to student-centered methods, heuristics may not be sufficient for re-engineering their classrooms. Table 1 compares a set of heuristic design principles and an algorithm for designing and sequencing key instructional events. Heuristics define basic principles or guidelines for solving problems (in this case, designing student-centered learning environments). Algorithms delineate a sequence of instructional events (or a step-by-step process) for facilitating learning. For instance, educators attempting to create a constructivist learning environment as posited by Honebein's (1996) must determine when instruction is to provide experiences, present multiple perspectives, embed learning within authentic context, and so forth. In contrast, educators applying Gagne's (1977, 1974) nine events of instruction must still operationalize each event, but the basic sequencing is already defined. Heuristics leave considerable room for interpretation, promoting creativity, and the development of alternative environments but they still require educators to formulate an instructional strategy for addressing each principle. A number of algorithms have been posited for applying behaviorist and cognitive information processing theories of learning (Hirumi, 2002), but there is a dearth of algorithms for creating student-centered and constructivist learning environments. The major of published guidelines for creating student-centered, constructivist learning environments are heuristic in nature. Algorithms are now needed to help educators apply constructivist design principles and to generate, text, and refine strategies that will help transform traditional, teacher-directed methods into more student-centered approaches to teaching and learning. …

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 123 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.