Can educational technology effectively differentiate instruction for reader profiles?
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| Title: | Can educational technology effectively differentiate instruction for reader profiles? |
|---|---|
| Authors: | Baron, Lauren S.1,2,3 (AUTHOR), Hogan, Tiffany P.1 (AUTHOR) thogan@mghihp.edu, Schechter, Rachel L.2,4 (AUTHOR), Hook, Pamela E.2 (AUTHOR), Brooke, Elizabeth C.2 (AUTHOR) |
| Source: | Reading & Writing. Nov2019, Vol. 32 Issue 9, p2327-2352. 26p. |
| Subject Terms: | *Educational technology, *Teaching, *Individualized instruction, *School year, *Online education, Children with dyslexia |
| Abstract: | Teachers are responsible for identifying and instructing an increasingly diverse population of student readers. Advances in educational technology may facilitate differentiated instruction. Using data from a large, population-based sample of third-grade students, we investigated what works for whom in technology-based literacy instruction. We classified 594 students into four reader subgroups or profiles based on two scores from a readily-available, commonly-used progress monitoring tool (aimsweb©). Using this simple and accessible method, we identified profiles of readers that resemble the poor decoder, poor comprehender, mixed deficit, and typical reader subgroups found in past studies that used extensive test batteries or advanced statistics. We then employed nonparametric analyses to examine both proximal and distal outcomes across one academic year. First, we compared the relative progress of reader profiles on a technology-based reading program (Lexia® Core5® Reading). Second, we determined whether each reader profile made gains on aimsweb at the end of the year. We found that Core5 effectively differentiated online instruction and contributed to improved aimsweb performance for most reader profiles. The findings from this study help inform educational best practices for efficient identification of and effective intervention for all students. [ABSTRACT FROM AUTHOR] |
| Copyright of Reading & Writing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 139095465 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Can educational technology effectively differentiate instruction for reader profiles? – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Baron%2C+Lauren+S%2E%22">Baron, Lauren S.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hogan%2C+Tiffany+P%2E%22">Hogan, Tiffany P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> thogan@mghihp.edu</i><br /><searchLink fieldCode="AR" term="%22Schechter%2C+Rachel+L%2E%22">Schechter, Rachel L.</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hook%2C+Pamela+E%2E%22">Hook, Pamela E.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brooke%2C+Elizabeth+C%2E%22">Brooke, Elizabeth C.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Reading+%26+Writing%22">Reading & Writing</searchLink>. Nov2019, Vol. 32 Issue 9, p2327-2352. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Teaching%22">Teaching</searchLink><br />*<searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br />*<searchLink fieldCode="DE" term="%22School+year%22">School year</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Children+with+dyslexia%22">Children with dyslexia</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Teachers are responsible for identifying and instructing an increasingly diverse population of student readers. Advances in educational technology may facilitate differentiated instruction. Using data from a large, population-based sample of third-grade students, we investigated what works for whom in technology-based literacy instruction. We classified 594 students into four reader subgroups or profiles based on two scores from a readily-available, commonly-used progress monitoring tool (aimsweb©). Using this simple and accessible method, we identified profiles of readers that resemble the poor decoder, poor comprehender, mixed deficit, and typical reader subgroups found in past studies that used extensive test batteries or advanced statistics. We then employed nonparametric analyses to examine both proximal and distal outcomes across one academic year. First, we compared the relative progress of reader profiles on a technology-based reading program (Lexia® Core5® Reading). Second, we determined whether each reader profile made gains on aimsweb at the end of the year. We found that Core5 effectively differentiated online instruction and contributed to improved aimsweb performance for most reader profiles. The findings from this study help inform educational best practices for efficient identification of and effective intervention for all students. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Reading & Writing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=139095465 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11145-019-09949-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2327 Subjects: – SubjectFull: Educational technology Type: general – SubjectFull: Teaching Type: general – SubjectFull: Individualized instruction Type: general – SubjectFull: School year Type: general – SubjectFull: Online education Type: general – SubjectFull: Children with dyslexia Type: general Titles: – TitleFull: Can educational technology effectively differentiate instruction for reader profiles? Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Baron, Lauren S. – PersonEntity: Name: NameFull: Hogan, Tiffany P. – PersonEntity: Name: NameFull: Schechter, Rachel L. – PersonEntity: Name: NameFull: Hook, Pamela E. – PersonEntity: Name: NameFull: Brooke, Elizabeth C. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 09224777 Numbering: – Type: volume Value: 32 – Type: issue Value: 9 Titles: – TitleFull: Reading & Writing Type: main |
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