Challenge and potential of fine grain, cross-institutional learning data

Alan Dix

doi:10.1145/2876034.2893429

Challenge and potential of fine grain, cross-institutional learning data

^*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

4 Dyfyniadau (Scopus)

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While MOOCs and other forms of large-scale learning are of growing importance, the vast majority of tertiary students still study in traditional face-to-face settings. This paper examines some of the challenges in attempting to apply the benefits of large-scale learning to these settings, building on a growing repository of cross-institutional data.

Iaith wreiddiol	Saesneg
Teitl	L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale
Cyhoeddwr	Association for Computing Machinery, Inc
Tudalennau	261-264
Nifer y tudalennau	4
ISBN (Electronig)	9781450337267
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1145/2876034.2893429
Statws	Cyhoeddwyd - 25 Ebr 2016
Cyhoeddwyd yn allanol	Ie
Digwyddiad	3rd Annual ACM Conference on Learning at Scale, L@S 2016 - Edinburgh, Y Deyrnas Unedig Hyd: 25 Ebr 2016 → 26 Ebr 2016

Cyfres gyhoeddiadau

Enw	L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale

Cynhadledd

Cynhadledd	3rd Annual ACM Conference on Learning at Scale, L@S 2016
Gwlad/Tiriogaeth	Y Deyrnas Unedig
Dinas	Edinburgh
Cyfnod	25/04/16 → 26/04/16

Mynediad at Ddogfen

10.1145/2876034.2893429

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

@inproceedings{d44ef121e19a41faa0219524ba40ba5e,

title = "Challenge and potential of fine grain, cross-institutional learning data",

abstract = "While MOOCs and other forms of large-scale learning are of growing importance, the vast majority of tertiary students still study in traditional face-to-face settings. This paper examines some of the challenges in attempting to apply the benefits of large-scale learning to these settings, building on a growing repository of cross-institutional data.",

keywords = "Education technology, Learning analytics, Linked data, MOOCs, OER, Reading lists",

author = "Alan Dix",

year = "2016",

month = apr,

day = "25",

doi = "10.1145/2876034.2893429",

language = "English",

series = "L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale",

publisher = "Association for Computing Machinery, Inc",

pages = "261--264",

booktitle = "L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale",

note = "3rd Annual ACM Conference on Learning at Scale, L@S 2016 ; Conference date: 25-04-2016 Through 26-04-2016",

}

Dix, A 2016, Challenge and potential of fine grain, cross-institutional learning data. yn L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale. L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale, Association for Computing Machinery, Inc, tt. 261-264, 3rd Annual ACM Conference on Learning at Scale, L@S 2016, Edinburgh, Y Deyrnas Unedig, 25/04/16. https://doi.org/10.1145/2876034.2893429

Challenge and potential of fine grain, cross-institutional learning data. / Dix, Alan.
L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale. Association for Computing Machinery, Inc, 2016. t. 261-264 (L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

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AB - While MOOCs and other forms of large-scale learning are of growing importance, the vast majority of tertiary students still study in traditional face-to-face settings. This paper examines some of the challenges in attempting to apply the benefits of large-scale learning to these settings, building on a growing repository of cross-institutional data.

KW - Education technology

KW - Learning analytics

KW - Linked data

KW - MOOCs

KW - OER

KW - Reading lists

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BT - L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale

PB - Association for Computing Machinery, Inc

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