Chemistry Reference
In-Depth Information
Efficacy-Enhancing Teaching
Because the effectiveness of Bandura
s(
1977
,
1997
) four sources of self-efficacy information has been demonstrated by research,
I hypothesized that
efficacy
-
enhancing teaching
, as will be explained in greater
detail below, can boost students
'
chemistry self-efficacy. Efficacy-enhancing teach-
ing refers to the use of instructional strategies during regular chemistry teaching,
which can provide students with performance accomplishments, vicarious experi-
ences, verbal persuasions, and positive physiological states. Specifically, efficacy-
enhancing teaching consisted of the following instructional strategies in my Hong
Kong study:
'
• Performance accomplishments—teach students how to find main ideas to solve
chemistry problems successfully.
• Vicarious experiences—provide students with opportunities to learn from
classmates.
• Verbal persuasion—praise students who are showing improvement on their
learning; tell students that they have the capability to learn chemistry better.
• Physiological and emotional states—encourage low-achieving or shy students to
participate in the learning process, provide students with a friendly learning
environment, and encourage students to ask and answer questions.
Eight items were used to measure student perceptions of the implementation of
efficacy-enhancing teaching in the chemistry classroom. They were prefaced with
the heading “In the Secondary-4 chemistry lessons since September 2011,” and
students were asked to rate each item on a 4-point scale (from 1
in
most lessons). The data on efficacy-enhancing teaching were of high reliability
(
never to 4
¼
¼
0.87). The AMOS software program (Byrne,
2010
) was used to assess the
univariate skewness and kurtosis of each item, as well as the joint multivariate
kurtosis. As can be seen in Table
3
, the univariate skewness and kurtosis were low,
but the joint distribution was multivariately non-normal. Thus, confirmatory factor
analysis was conducted using the asymptotic distribution-free estimation in AMOS.
The ability of a one-factor model to fit data was evaluated using the chi-square (
ʱ
¼
2
),
the goodness-of-fit index (GFI), the adjusted GFI (AGFI), the Tucker-Lewis index
(TLI), the comparative fit index (CFI), and the root mean square error of approx-
imation (RMSEA). Because the
ˇ
2
statistic is sensitive to sample size, I based the
evaluation of model fit on the considerations of multiple indexes and beyond the
statistical significance of the
ˇ
2
. According to conventional criteria, an acceptable
ˇ
fit
is
indicated by GFI
0.90, AGFI
0.90, TLI
0.90, CFI
0.90, and
>
>
>
>
RMSEA
0.08 (Schumacker & Lomax,
2010
). Confirmatory factor analysis of
the Hong Kong student data indicated mediocre fit for a one-factor model
(
<
2
ˇ
53.937, df
20,
p
0.001, GFI
0.963, AGFI
0.934, TLI
0.803,
¼
¼
<
¼
¼
¼
CFI
0.054) because TLI and CFI were below the preferred
value. Research is being conducted to improve the items.
0.859, RMSEA
¼
¼
Chemistry Self-Efficacy
The second measure aimed to assess students
self-
efficacy for learning school chemistry. To make my self-efficacy scale domain
specific and task specific, I selected five items from Table
1
that matched the
'
Search WWH ::
Custom Search