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SUMMARY:Clustering educational data: a high school students' performance a
nalysis
DTSTART;VALUE=DATE-TIME:20230131T105500Z
DTEND;VALUE=DATE-TIME:20230131T112000Z
DTSTAMP;VALUE=DATE-TIME:20240719T053020Z
UID:indico-contribution-152-636@cern.ch
DESCRIPTION:Speakers: Matteo Farnè (University of Bologna)\nIn this talk\
, we first briefly discuss the definitions of Educational Data Mining and
Learning Analytics\, providing an overview of the most used statistical me
thods in both fields\, as well as a synopsis of their differences and simi
larities. The possible aims of the different methods\, aimed at uncovering
hidden patterns in educational data\, are stressed pointing to the possib
le services provided to the whole school community. In the following\, we
focus on clustering methods for educational data: we start from traditiona
l methods (hierarchical\, partitive\, and density-based)\, we proceed with
dimension reduction methods\, such as factorial k-means and reduced k-mea
ns\, and we present some methods which incorporate the longitudinal dimens
ion. Then\, we present a pilot analysis carried out on a dataset reporting
the performance of a class of high school students in three periods (whic
h were treated as three separate datasets)\, using hierarchical clustering
\, partitive clustering (k-means)\, factorial k-means and reduced k-means
techniques. The goal of the analysis is to show how the composition of gro
ups and the number of groups vary in each period and which are the factors
that influence the creation of groups. The partitions obtained with these
algorithms were compared in terms of reliability using the average silhou
ette width index. Reduced k-means and k-means generated similar results an
d we can say that these results were the most acceptable considering the a
verage silhouette width. Hierarchical clustering generated the same result
s as the former algorithms only in the first two periods of time. The resu
lts generated by factorial k-means differ from the other methods and as su
ggested by the values of the average silhouette width\, it is not the best
algorithm for clustering on the dataset available to us. The underlying m
eaning of clusters over time and the reasons behind statistical results ar
e discussed and analyzed in detail\, with the aim to highlight possible st
udent group structures present in a high school class.\n\nhttps://indico.u
nina.it/event/60/contributions/636/
LOCATION:Complesso S. Marcellino e Festo G4
URL:https://indico.unina.it/event/60/contributions/636/
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SUMMARY:An application of Differential Item and Bundle Functioning analysi
s to the study of gender differences in Mathematics Education: implication
s for educational practitioners.
DTSTART;VALUE=DATE-TIME:20230131T103000Z
DTEND;VALUE=DATE-TIME:20230131T105500Z
DTSTAMP;VALUE=DATE-TIME:20240719T053020Z
UID:indico-contribution-152-634@cern.ch
DESCRIPTION:Speakers: Clelia Cascella (INVALSI)\nThe study of gender diffe
rences in mathematics attainment has exponentially increased over time as
closing the gap between boys and girls is a priority for both research and
policy. Mathematics has in fact been listed by the European Commission as
one of the ‘key competences’ necessary to all for personal fulfilment
and development. Nonetheless\, there are still several countries around t
he world in which boys significantly outperform girls in mathematics with
negative implications at both individual and collective level (i.e.\, in t
erms of expected employability and wage as well as in terms of societal an
d economic development\, especially considering that the demand of STEM-re
lated job is going to increase).\nThe current research builds on a Marie C
urie project aimed at exploring the possible association between gender di
fferences (particularly\, female underachievement) in mathematics and envi
ronmental socio-cultural and economic factors. Building on results showing
that the more traditional the ‘field’ in which students grow their ge
nder identity\, the better the boys’ attainment in mathematics compared
with girls\, the current study goes a step further by showing how measurem
ent can be used to help educational practitioners in understanding the rel
ationship between students’ gender and their attainment in mathematics.\
nWithin the framework of the Rasch analysis\, educational research has fre
quently employed Differential Item Functioning (DIF) analysis to measure t
he (possible) association between the probability of successfully encounte
ring each single (mathematics) item and a single students’ characteristi
c (such as gender). Differential Functioning occurs when examinees from di
stinct groups\, but matched on ability\, have different probabilities of a
nswering an item correctly. Differential functioning can relate to a singl
e item or to a bundle of items. The former is referred as Differential Ite
m Functioning (DIF)\, the latter as Differential Bundle Functioning (DBF).
\nIn the current study\, after showing results from a systematic literatu
re review about the employment of DBF in educational research\, I present
an application of both DIF and DBF to Large-Scale Assessment (LSA) data\,
collected in Italy by the Italian national institute for the evaluation of
educational system\, in 2017\, at Grade 10 (on average\, 15-years old stu
dents). Results were used to discuss (i) strengths and weaknesses of both
DIF and DBF\; (ii) similarities and differences of these two analyses and\
, thus\, how each of them can be employed to support educational practitio
ners in dealing with gender differences\; as well as (iii) the use of LSA
data in Mathematics Education research.\n\nhttps://indico.unina.it/event/6
0/contributions/634/
LOCATION:Complesso S. Marcellino e Festo G4
URL:https://indico.unina.it/event/60/contributions/634/
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SUMMARY:Mathematics achievement at the end of upper secondary school durin
g COVID-19 pandemic: insights from the INVALSI national assessment
DTSTART;VALUE=DATE-TIME:20230131T100500Z
DTEND;VALUE=DATE-TIME:20230131T103000Z
DTSTAMP;VALUE=DATE-TIME:20240719T053020Z
UID:indico-contribution-152-635@cern.ch
DESCRIPTION:Speakers: Marta Desimoni (INVALSI)\nEducation stakeholders in
times of the Covid-19 pandemic require a broad empirical base to recover f
rom the crisis and to strengthen the resilience of education systems in th
e future. National assessments of students learning outcomes are potential
ly useful sources of education data during the COVID-19 pandemic\, playing
a pivotal role in monitoring school systems and improving education quali
ty. In Italy\, the National Institute for the evaluation of the education
and training educational system (INVALSI) every year carries out a standar
dized national assessment of students’ achievements in primary and secon
dary education. From the school year (SY) 2018-2019\, the INVALSI national
testing program has been extended to the last year of upper secondary sch
ool\, thus allowing to depict of an overall picture of students’ mathema
tics achievement at a key point of transition to tertiary education and em
ployment. Results are reported not only in terms of numerical scores but a
lso as proficiency levels\, to offer substantial information on the profic
iency status at the system level. The attribution of an explicitly describ
ed level is also supposed to allow the students\, their families\, and the
teachers to have more significant and useful feedback compared to a simpl
e score\, thanks to the direct link to the content area covered by the tes
t. The present work aims at providing an overall picture of students’ p
erformance in the INVALSI mathematical assessment at the end of upper seco
ndary school in the school year 2020-21\, about one year after the COVID-1
9 outbreak in Italy\, taking into account their pre-pandemic mathematics p
roficiency level (Grade 10\, s.y. 2017-18). Protective factors and possibl
e sources of inequalities in achievements during the COVID-19 crisis are a
lso explored\, by adopting a multilevel approach. The overall differences
between pre-pandemic and pandemic cohorts in Italy suggest a pandemic achi
evement gap in mathematics at the end of upper secondary school\, with a h
igher number of students resulting as low performers\, i.e. “who are lik
ely to use basic skills and procedures mainly acquired in lower secondary
school and\, partly\, at the end of the first two years of upper secondary
school […]” or maximum “knowing the basic mathematical concepts as
outlined in the national guidelines for mathematics in the first two years
of upper secondary school” […]. Considering their pre-pandemic starti
ng points (G10) in a retrospective perspective\, it emerges that although
most of the G13 low performers were already struggling with mathematics\,
about one out of three moved from intermediate-high (G10 scale) to lowest
levels (G13 scale). More encouraging results are those from another subgro
up of pandemic-cohort students who maintained intermediate to high perform
ance in mathematics (with respect to G10 and G13 scales)\, suggesting posi
tive patterns of adaptation in the context of adversity due to the COVID-1
9 crisis. Multilevel analyses provide further insights into the relevance
of different variables in supporting the students’ relative progresses i
n mathematics during the pandemic\, both at the individual and the context
ual level.\n\nhttps://indico.unina.it/event/60/contributions/635/
LOCATION:Complesso S. Marcellino e Festo G4
URL:https://indico.unina.it/event/60/contributions/635/
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