Page 32 - Šolsko polje, XXVII, 2016, no. 3-4: IEA ICILS in druge sodobne teme, ur. Eva Klemenčič
P. 32
šolsko polje, letnik xxvii, številka 3–4
That is, not each student faces every single task (Fraillon, 2015). In turn,
the CIL scores could not be computed using the well-known methods
from the Classical Test Theory (CTT) or Item Response Theory (IRT).
Instead, the CIL scores in ICILS 2013 were obtained by the study center
as five “plausible values” (PVs). In brief, the item parameters were estimat
ed using different IRT models, depending on the scoring of the items (di
chotomous or partial credit). The item parameters were used along with
student responses on achievement items and the principal components of
the background items to form conditional distribution where the infor
mation of the items the students did not face was imputed using the in
formation of the background variables. The final scores for each student
were drawn at random five times (PVs) from the distribution of the scores
of students with similar background characteristics (Gebhardt & Schulz,
2015).
The SES measure used in this study is the National index of students’
socioeconomic background (S_NISB). The index was derived from the
students’ parental highest occupational status, students’ parental highest
educational attainment and the number of books in the students’ home.
The index was created using Partial Credit Model (PCM), part of the
broad IRT framework (Schulz & Friedman, 2011).
Individual student, student home and school variables were used as
well in the regression models (see Analytical methods). The groups of dif
ferent variables are as follows:
– Individual characteristics related to CIL: expected further educa
tion; basic skills ICT self-efficacy; advanced skills ICT self-efficacy;
attitudes towards ICT.
– ICT use at home and school: frequency of computer use at home,
school and other locations; use of ICT for different purposes, in
cluding study purposes.
– Home and school ICT resources, emphasis and use of ICT in teach
ing and learning: availability of computers and network connection
at home; principal’s views on the importance of using ICT; ICT use
for teaching and learning activities at school; monitoring of teach
er use of ICT in pursuing learning outcomes; ICT management and
resources; teacher professional variables (school principal responses).
The full list of all used variables with their description and measure
ment characteristics can be found in the Appendix.
30
That is, not each student faces every single task (Fraillon, 2015). In turn,
the CIL scores could not be computed using the well-known methods
from the Classical Test Theory (CTT) or Item Response Theory (IRT).
Instead, the CIL scores in ICILS 2013 were obtained by the study center
as five “plausible values” (PVs). In brief, the item parameters were estimat
ed using different IRT models, depending on the scoring of the items (di
chotomous or partial credit). The item parameters were used along with
student responses on achievement items and the principal components of
the background items to form conditional distribution where the infor
mation of the items the students did not face was imputed using the in
formation of the background variables. The final scores for each student
were drawn at random five times (PVs) from the distribution of the scores
of students with similar background characteristics (Gebhardt & Schulz,
2015).
The SES measure used in this study is the National index of students’
socioeconomic background (S_NISB). The index was derived from the
students’ parental highest occupational status, students’ parental highest
educational attainment and the number of books in the students’ home.
The index was created using Partial Credit Model (PCM), part of the
broad IRT framework (Schulz & Friedman, 2011).
Individual student, student home and school variables were used as
well in the regression models (see Analytical methods). The groups of dif
ferent variables are as follows:
– Individual characteristics related to CIL: expected further educa
tion; basic skills ICT self-efficacy; advanced skills ICT self-efficacy;
attitudes towards ICT.
– ICT use at home and school: frequency of computer use at home,
school and other locations; use of ICT for different purposes, in
cluding study purposes.
– Home and school ICT resources, emphasis and use of ICT in teach
ing and learning: availability of computers and network connection
at home; principal’s views on the importance of using ICT; ICT use
for teaching and learning activities at school; monitoring of teach
er use of ICT in pursuing learning outcomes; ICT management and
resources; teacher professional variables (school principal responses).
The full list of all used variables with their description and measure
ment characteristics can be found in the Appendix.
30
