implications of mediated instruction to remote learning in mathematics joy l. matthews-lópez...

Unpublished work © 2002 by Educational Testing Service

Implications of Mediated Instruction to Remote Learning in

Mathematics

Joy L. Matthews-López

Educational Testing Service

Sergio R. López-Permouth

David Keck

Ohio University


2

Background

• Basic math skills should be in place prior to entrance into college/university because students need to be able to satisfactorily complete prerequisite course work

• Higher Ed. $ resources are limited– Faculty– Classroom space

• Redundancy is hard to justify to financiers


3

Questions

• If given a choice, do students naturally gravitate to a remote learning environment?

• Is class attendance positively correlated with class performance?


4

The traditional approach…

• Students attend class• Faculty lecture• Specific time is designated for office hours• All students are taught at the same pace, on

the same schedule• Little formative feedback is provided to the

students• Class sizes tend to be large


5

The mediated approach…

• Designates the instructor as a facilitator of learning rather than a deliverer of information

• Permits students to advance at their own pace

• Personalizes the presentation of material

• Lends itself well to a remote learning model


6

Methodology• 3 sections of Math 101 students (with 20 students

per section)– Section 1: full attendance was required (3 days + a test)– Section 2: partial attendance was required (1day + a

test)– Section 3: no attendance was required (test only)

• A custom-designed Basic Skills Test (BST) was administered to all students on the first day of class (r = .92).– The BST consisted of 20 constructed response items.– Content consisted of arithmetic and basic pre-algebra.


7

Data

• 5 measures per student*:– Daily attendance (recorded by instructors)

– Daily time spent on-line (recorded by the software)

– Pre-test score (BST) (given on the 1st day of class)

– Post-test score (BST) (given on the last day of class)

– Final course grade (7 common tests + final exam)

– Summative course evaluation (specifically made for this project)

(*IRB approval was sought and obtained.)


8

Analyses

• Pre-test Scores– ANOVA

• Used to determine initial group equivalence

• Attendance was recorded by each instructor in order to ensure adherence to attendance policies.


9

Time Spent On-line

• Time spent on-line was automatically recorded by the software.– This information was collected to see how

students in the different groups spent their study time.

– ANOVA


10

Analyses, cont.

• Post-test Scores– ANOVA (to examine for post-course group

mean differences)

– Filtered correlations (to examine with-in group relationships between pre- and post-test scores)


11

Analyses, cont.• Gain scores

– Computed to be the difference between pre- and post-test scores

– ANOVA• Performed to examine for between-group differences.

• Final Course Grades– ANOVA

• Time Spent On-line– ANOVA

• to examine for between-group mean differences


12

Results• Pre-test Scores: no group differences

(F = .056, p = .946 at α = .05).

Table 1

Descriptive Statistics for Pre-test Scores*

Group N Mean Std. Deviation

1 18 11.78 3.32

2

3

19

18

12.16

12.00

3.20

3.86

*This finding was used to establish initial group equivalences.


13

Results

• Attendance– All students were found to be in compliance

with the attendance policies for their particular section.


14

Results

• Gain scores: (F = 2.88, p = .065, α = .05)

Group Mean Gain

Scores

Standard Error

1

2

3

4.22

2.61

2.11

.76

.70

.46


15

Gain Scores, cont.Counts:

Group 1

0 6 6 7 Pre (13)

0 0 12 7 Post (19)

Group 2

0 3 11 4 Pre (15)

0 1 6 11 Post (17)

Group 3

1 5 9 4 Pre (13)

0 0 6 13 Post (19)

0 2015105Raw Score


16

Results

• Pre-test to post-test correlations

Within-group Correlations

Group N Correlation

1

2

3

18

19

19

.475*

.510*

.856*

*Statistically significant at α = .05, 2-tailed


17

With-in Group Correlations

PRE

18161412108642

PO

ST

22

20

18

16

14

12

10

8Group 1

PRE

20181614121086

PO

ST

20

18

16

14

12

10

8Group 2

PRE

201816141210864

PO

ST

22

20

18

16

14

12

10

8Group 3

Low:High

Low:Low

Group 1 Group 2

Group 3


18

Time Spent On-line: Results

• Regardless of group membership, students spent approximately the same amount of time on-line, overall.

• There were different patterns to time spent on-line.

– Group 1…steady usage

– Group 2…heavier usage prior to test day

– Group 3…heaviest usage immediately prior to test day


19

Post-test Scores: Results• Group mean scores were not significantly different

from one another (F = 1.937, p = .154, α = .05).

Group N Mean Post-test Score

1

2

3

18

19

19

15.95

14.83

14.11


20

Final Grades: Results• Final grade mean scores were not significantly

different across groups (F = .059, p = .943, α = .05)

Group Final Grades

1

2

3

82.11

82.89

82.11


21

Conclusions

• Students enrolled in a mediated learning environment naturally gravitate to a remote learning model– i.e. When given the option, students chose not

to attend class.

• Academic performance did not appear to be adversely impacted by the remote learning environment– …however, what about learning?


22

Recommendations

• Students scoring lower than a 70% on the BST should be required to attend class, whether mediated or not.

• Students scoring 70% or higher should be allowed to utilize a remote mediated learning model.


23

Limitations

• Sample size is small.

• Results may be course specific.

• Remedial vs. non-remedial courses should be examined.

implications of mediated instruction to remote learning in mathematics joy l. matthews-lópez...

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