Deep Learning Practice Resolves Retention Issues

 This article expands on my recent January “Content Timing Process Realized” and March 2022 blogs on “Deep Learning Applied” findings, to elucidate on how learning retention can be actualized through applied parallel thought (Erland, J. K. February 4, 1986); Rumelhart. D. E. McClelland, J. 1986), and neurological codes, (Hinton, G. 2006). Looping, puppetry dramatization becomes a key deep memory element for re-training career and academic skill retention (Erland, J. K. 1980).

 A highly skilled workforce is a requirement in today’s demanding technological economy. Business and industry now grapple how to create upskilling training that retains and advances eager workers in need of procedural learning. Many have ingrained lack of focus creating erratic behavior and follow-through with written and oral directions that underlie all procedural details.

 Working memory becomes the impetus for activating layered segmented chunks, rotating in spans or units, known as “Deep Learning”, earlier referenced as “Contrapuntal, Sweeping, or Parallel Thinking”© (Erland, Janis L., 1986) in my early writings. This innovative Deep Learning, cognitive process is a vitally needed retention component for up-skilling and re-skilling training. Deep Learning offers a critical component for planning, making coherent decisions, and expressing newly learned skills.

 As a conduit to create the procedural system outcome, are “Deep Learning” practice sessions. Art, science, and computational skills are provided by innovative ventriloquist, prosody speaking, puppets. The participant assumes the role of detecting new patterns and systems.

 The Bridge to Achievement’s (BTA)™ mental agility, a cognitive, span-expansion coding process, has been documented through serial published, juried, award-winning, longitudinal experimental research for academic and career achievement. Outstanding outcomes were documented in math, reading and language skills.

Additionally, the extensive longitudinal data research revealed new mental strength will sustain the enhanced skills over time, when applied consistently. The BTA™ Deep Learning practice becomes a valued supplemental front engine for all reading, math, and language programs, or used independently as a “stand alone, mental jump-starter”. Subsequently, the intense, Deep Learning rehearsal process creates a new, higher functioning, and more optimistic, empowered individual.

 The unique BTA content elements cement learning retention in multiple ways:

 -     Brief, timed, self-paced lessons. Mental focus maintained through ongoing fixed, focal interest.

-     Original, one-of-a-kind, phonetic and coding practice lessons.

      -     Lessons increase gradually in complexity with locked, timing, pacing.

      -     Fourteen to thirty minute short, segmented, daily lessons offer less time involvement.

      -     Whole-brain, peers and puppets, modeling rehearsal regimen (Erland, J. K.  1980).

      -     Authentic, Hollywood Golden Age ventriloquist puppets applied as adjacent role models.

      -     Thirteen choreographed character positions rotate in loops over 800 unique segments.

      -     Solid, verified, data-based published results with multiple 3^rd party reviewers (Erland, J. K. Fall 2000).

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Erland, J. K. (1980). Vicarious modeling using peers and puppets with learning disabled adolescents in following oral directions. The University of Kansas, Lawrence, Kansas.

Erland, Janis L. (February 4, 1986; copyright TXu 225 862). Contrapuntal Thinking and Definition of Sweeping Thoughts.

Erland J. K. (c 1989), Hierarchy of Thinking. Mem-ExSpan, Inc.

Erland, J. K. (Fall, 1998). Cognitive skills and accelerated learning memory training using interactive media improves academic performance in reading and math.  Journal of Accelerative Learning and Teaching,  23, (3 & 4), 3-57.

Erland, J. K. (Fall 2000). Brain-Based Longitudinal Study Reveals Subsequent High Academic Achievement Gain for Low-Achieving, Low Cognitive Skills, Fourth Grade Students. Journal of Accelerated Learning and Teaching. 25, (3&4) pp.5-48. ERIC ED # 453-553. & # CS 510 558. https://Books.Google.com/jankuypererland page 41.

Erland, J. K. (© 2008). Downloadable, unpublished report. Five Generations, 27-years of iterative Brain-Based Accelerative Learning Experimentation Demonstrate Cognitive Skill Improvement Enhances Academic and Career Goals. (https://memspan/jalt).

Hinton, G. (2006). Deep Learning and the recipient of the 2001 Rumelhart Deep Learning Prize.

Rumelhart, D. E., McClelland, J. and the PDP Research Group. (1986).  Parallel distributed processing:  Explorations in the micro structure of cognition. Cambridge, MA: MIT Press.