A Meta-Analysis of Educational Data Mining on Improvements in Learning Outcomes.
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Notice bibliographique
Résumé
A meta-synthesis study was conducted of 60 research studies on educational data mining (EDM) and their impacts on and outcomes for improving learning outcomes. After an overview, an examination of these outcomes is provided (Romero, Ventura, Espejo, & Hervas, 2008; Romero, et al., 2011). Then, a review of other EDM-related research published after 2008 (88 studies) was completed. Thirty-nine of those studies also offered an overview of EDM's impact on learning outcomes. In addition, 12 of the 39 studies investigated the efficacy of EDM for learning outcomes. EDM characteristics (i.e., tools, techniques, models, procedures, measures, and results) were examined in each of the 12 studies. Ninety-four of the total 148 studies showed positive results for EDM. Directions for future research are discussed. Keywords: Educational data mining, learning outcomes, prediction, classification Introduction Recently, the concept of educational data mining (EDM) has witnessed dramatic worldwide growth in the field of education. EDM has gained increased attention as a process that provides useful data necessary for decision-making in education institutions (Kusiak, 2002). Specifically, teacher education programs are recognizing EDM as a useful analytical tool that may lead to improvements in learning outcomes. Also, research (i.e., Al-Shammari, 2011; Ogundokun, 2011) has been placing great emphasis on the improvement of learning outcomes. Several research studies (e.g., Barros & Verdejo, 2000; Chapman & Bloxham, 2004; Devine, Hossain, Harvey & Baur, 2011; Minaei-Bidgoli, Kortmeyer & Punch, 2004; Ranjan & Ranjan, 2010; Thai-Nghe, Drumond, Horvath & Schmidt-Thieme, 2011; Xiong, Pardos & Heffeman, 2011; Yudelson, Medvedeva, Legowski, Castine, Jukic & Crowley, 2006) have investigated the effects of EDM on learning outcomes. Other research (Delavari, Beikzadeh, & Phon-Ammuaisuk, 2008) has cited EDM as a useful tool in improving learning outcomes due to its ability to identify at-risk students and predict their future performance in learning settings. Sparks (2011) found that EDM provides answers to questions relating to student performance. Campbell, DeBlois, and Oblinger (2007) also stated that EDM can answer the call for accountability through academic analytics, which is emerging as a new tool for a new era (p. 40). This research has three purposes: to provide an overview of EDM; to offer a brief overview of research on EDM; and to investigate and then discuss how the use of EDM improves learning outcomes. Overview of EDM EDM is a new tool used in education to uncover useful information in or hidden relationships among large amounts of electronic data stored in a school's system (Baker, 2011). EDM uses reliable techniques in multiple analytic procedures that ultimately reveal hidden information that helps teachers, administrators, and others in improving learning outcomes (Ogor, 2007). According to Romero and Ventura (2010), EDM answers questions related to what a student actually knows and whether a student is learning. EDM works much like Learning Analytics, except that EDM basically addresses the development of new methods of making discoveries for data analysis while Learning Analytics addresses only the application of known methods (Baker, 2011). EDM methods differ from other methods used in general data mining due to the complexity of interrelationships among types of data and data sets. For example, there are multiple levels of hierarchy in educational data (Baker, 2011; Campbell, DeBlois & Oblinger, 2007; Romero & Ventura, 2010). EDM borrows many of its applications from machine learning and artificial intelligence (Baker, 2011). By the late 1990s, data mining had split off from artificial intelligence to stand on its own (Romero, Ventura, Espejo, & Hervas, 2008). In 2008, the field of education officially witnessed the growth of EDM at the First Annual Conference on Educational Data Mining, which was held in Montreal, Quebec, Canada in June 20-21, 2008 (Winters, 2006). …
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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