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pith:QXF7SXSP

pith:2024:QXF7SXSPZJZLXULIE52VDOTYAH
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How do machines learn? Evaluating the AIcon2abs method

Cabral Lima, Fabio Ferrentini Sampaio, Priscila Machado Vieira Lima, Rubens Lacerda Queiroz

The AIcon2abs method using WiSARD raises participant satisfaction with understanding machine learning processes in a short hands-on course.

arxiv:2401.07386 v5 · 2024-01-14 · cs.CY · cs.AI · cs.LG

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4 Citations open
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Claims

C1strongest claim

Nearly all participants rated AIcon2abs positively, with the results demonstrating a high degree of satisfaction in achieving the intended outcomes of increasing understanding of machine learning.

C2weakest assumption

That positive satisfaction ratings and phenomenological analysis from a single six-hour remote course with 34 self-selected Brazilian participants accurately measure lasting gains in conceptual understanding of ML processes and generalize beyond this group and format.

C3one line summary

Evaluation of the AIcon2abs educational method using WiSARD shows high participant satisfaction after a six-hour remote course with 34 Brazilian participants including children and adults.

References

6 extracted · 6 resolved · 0 Pith anchors

[1] AIcon2abs Empirical Evaluation Data 2023 · doi:10.17632/ch6ys3gz82.1
[2] Academia Brasileira de Ciências 2024
[3] https://doi.org/10.1063/1.2810904 Fleming, N.D. (1995). I'm different; not dumb. Modes of presentation (VARK) in the tertiary classroom. Proceedings of the 1995 Annual Conference of the Higher Educati 1995 · doi:10.1063/1.2810904
[4] https://doi.org/10.3390/educsci1402017 Kaner, C., Bach, J., & Pettichord, B. (2001). Lessons learned in software testing. John Wiley & Sons. Kirk, R.E. (1978). Introductory statistics. Brooks/Cole Pub 2001 · doi:10.3390/educsci1402017
[5] Hodder Education. Medina, E.M. (2004). Beyond the ballot box: Computer science education and social responsibility. ACM SIGCSE Bulletin, 36(4), 7–10. https://doi.org/10.1145/1041624.1041626 Mitchell, 2004 · doi:10.1145/1041624.1041626
Receipt and verification
First computed 2026-06-04T01:08:25.063496Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

85cbf95e4fca72bbd168277551ba7801e00cb3e0a46e6093e706b0d223e23bbc

Aliases

arxiv: 2401.07386 · arxiv_version: 2401.07386v5 · doi: 10.48550/arxiv.2401.07386 · pith_short_12: QXF7SXSPZJZL · pith_short_16: QXF7SXSPZJZLXULI · pith_short_8: QXF7SXSP
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QXF7SXSPZJZLXULIE52VDOTYAH \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 85cbf95e4fca72bbd168277551ba7801e00cb3e0a46e6093e706b0d223e23bbc
Canonical record JSON
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    "submitted_at": "2024-01-14T22:40:58Z",
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