Learning Statement

Learning Statement

In recent years, the learning landscape has evolved significantly, driven by the COVID-19 pandemic, technological advancements, and changing educational needs. These trends collectively signify a shift towards more dynamic, flexible, and learner-centered environments, leveraging technology to not only enhance learning experiences but also to prepare students for future challenges. 

The Center explores and experiments with the following approaches to learning: Assessing learner performance and evaluating their progress. Educators can use a variety of assessment methods (e.g., formative assessments, summative assessments, performance tasks, portfolios) to gather evidence of students' mastery of the competencies. This information can then be used to provide feedback, guide instruction, and determine proficiency and/or other indicators of achievement. 

These strategies are designed to increase the efficiency and speed of learning while improving retention and understanding. These methods are often based on themes and research from cognitive psychology and educational theory. Our work explores among others, the development of mental models/schemas, metacognitive strategies, and the chunking and sequencing of concepts in support of practice-centered education and training solutions. 

The Center investigates the use of formative assessments to monitor student progress and adjust instruction accordingly. These assessments are designed to detail the areas of strengths and weaknesses of the leaner, are continually applied, and forward looking, used to guide the next learning path.  

To ensure effective learning, it is important to assess a learner’s readiness when designing and delivering educational and training programs. Enter readiness assessments, which are used to measure an individual's or organization's preparedness for a specific task or situation. They can measure a wide range of knowledge, skills, and abilities (e.g., cognitive abilities, physical fitness, technical proficiency) and are used in a variety of settings including education, military, and emergency management. In academic and training settings, they are primarily used to determine a student’s level of preparedness for learning new information or skills, as well as to identify what a learner already knows and what they need to learn to be successful in a particular subject or grade level. They can also be used to 1). Provide feedback on the learner’s conceptual understanding (e.g., a learner’s understanding of a concept within an instructional module could include assessment assignments where the learner’s conceptual understanding is verified), and 2). Provide feedback to automate individualized instruction. Once a learner's conceptual understanding is verified, this in turn can be used to automate how a Learning Management System, for example, then delivers the next set of (personalized/individualized) instructional material.  

Is an instructional technique for teaching and learning of hierarchical, sequential material. Material to be learned is subdivided into natural units or steps, covering from one lesson to several weeks’ lessons. Students are given a test at the end of the unit, and if they do not achieve a mastery grade on the test (typically 80-95%) they are provided with more time and more teaching until they can achieve a mastery grade on a retest (Arlin, 1984). 

Refers to the phenomenon where information is better remembered if it is generated by the learner rather than simply read. Benefits of this technique include

    1. Enhanced engagement and learning
    2. Helps develop metacognitive skills
    3. Provides feedback and revision
    4. Improved critical thinking and creativity
    5. Increased motivation
    6. Improves recall, enhances comprehension, and promotes application of knowledge in new contexts. 

https://www.edutopia.org/article/using-student-generated-questions-promote-deeper-thinking. 

References

  • Arlin, M. (1984). Time, Equality, and Mastery Learning. Review of Educational Research, 54(1), pp. 65-86. 

  • Chung, G.K.W.K. (2013). Toward the Relational Management of Educational Measurement Data. Princeton NJ: The Gordon Commission. 

  • Marzano, R. J., & Pickering, D. J. (1997). Dimensions of Learning: Teacher's Manual, 2nd Edition. Alexandria, VA: Association for Supervision and Curriculum Development. 

  • Marzano, R. J., & Kendall, J. S. (2006). The New Taxonomy of Educational Objectives. Thousand Oaks, CA: Corwin Press.

  • Marzano, R. J. (2007). The Art and Science of Teaching: A Comprehensive Framework for Effective Instruction. Alexandria, VA.