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 3rd party reviewers (Erland, J. K. Fall 2000).
_____________________
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.
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.