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Enregistrement W249890105

Predicting a Student's Success at a Post-Secondary Institution

2011· article· en· W249890105 sur OpenAlex

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Notice bibliographique

RevueJournal of applied research in the community college · 2011
Typearticle
Langueen
DomaineMathematics
ThématiqueMathematics Education and Programs
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMathematics educationTest (biology)Academic achievementPsychologyStandardized testMedical educationMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The primary objective of this study is to investigate the correlation between college math entry placement scores, required college math courses and the final program GPA of the students who have completed a diploma in a business or a technical program at an Ontario college (in Toronto, Canada) from 2000 to 2008. The correlation analysis did demonstrate a significant positive correlation between college math entry placement scores and final grades from the math courses of the students that have completed the program. A significant positive correlation was also demonstrated between the college math placement scores and the final program GPA of the same sample of students. All of these findings support the use of math placement tests to predict the success of students in a post-secondary environment. Introduction It is common practice to use standardize tests to set admission requirements for acceptance into a postsecondary program. Standardized tests have also been used within the post-secondary environment as knowledge assessments and predictors of college achievement. As knowledge assessments, these types of tests have found considerable success (Armstrong, 2000, McFate & Olmsted, 1999, Bunce & Hutchinson, 1993, Tai et. al, 2006, Russell, 1994) and unlike admission tests, the goals of these tests are to measure a student's academic ability and then properly stream students to courses to ensure academic success and improve institutional outcomes. Post-secondary institutions use standard placement tests that tend to focus on math and English assessment and are used to place the incoming student in an appropriate learning stream to promote student success at that institution. Susan Callahan (1993) presented a paper that concluded that math placement tests provide accurate course recommendations for incoming students and also showed that these placement tests assist with curriculum improvements within Cottey College in Nevada, Missouri. Roth, Crans, Carter, Ariet and Resnick (2000) did a similar study with a sample of almost 20,000 high school graduates in Florida and found that placement tests were beneficial to students who might have difficulties in college math courses and thereby providing the proper support to meet students' needs. This study agreed with the Callahan work (1993) and supported the ability to identify areas of an academic weakness or gaps in student knowledge; begin to stream the students early in their post-secondary career into the appropriate learning stream; and will ultimately improve student success at a post-secondary institution. In yet another study (Leopold and Edgar (2008)) a math test taken during the first week of the students' secondsemester introductory college chemistry class was used to predict success within the chemistry class and showed a high degree of direct correlation between the math tests scores and final chemistry grade. This work demonstrated mat math ability can be a predictor to other post-secondary non-math course grades. Routinely, post-secondary institutions administer entry-level placement tests that are critical to a student's academic options, high stakes and are used to give the students an appropriate context for the level of learning that is expected in the class. Armstrong's work (2000) supports the use of placement tests to support student learning outcomes, but he cautions the use of streaming of students based only on one measure. This message is echoed in McFate «Sc Olmsted's paper (1999) which suggests that institutions find a middle ground by using placement-test results only as an advising tool or including additional placement factors. Both studies demonstrate that placement tests support student outcomes, but that their results must be balanced with other factors that are part of the students' background that influences their success in a course. Previous work (Armstrong, 2000, Callahan, 1993, Bunce & Hutchinson, 1993, Tai et al, 2006, Russell, 1994, McFate & Olmsted, 1999) has demonstrated that placement testing can be beneficial to the students' learning, college outcomes and institutional retention numbers. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,164
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0200,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,310
Tête enseignante GPT0,452
Écart entre enseignants0,142 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle