The project will also have a number of implications for key educational issues.
The software tools based on the project’s theoretical and empirical contributions will be made available online, to be used by teachers and students at the end of secondary education and at the beginning of university, to profile learners’ strengths and weaknesses in reading comprehension and the complexity of their writing.
This offers an opportunity for formative, diagnostic (self)assessment, and at the same time the possibility to assess Linguistic Complexity (LC)/Processing Difficulty (PD) of reading materials such as textbooks. It should be emphasized that there is currently no software with a comparable set of functionalities, for Italian or any other language.
LuCET for the INVALSI assessment
These software tools and results from LuCET will also be relevant to improve INVALSI’s large-scale assessment (LSA) design and score reporting. National assessments are designed to describe achievement levels of an entire education system, and several countries have been using data from these studies to enhance their educational practice and policy. The last decade has witnessed a growing recognition of the need for enhancing educational assessment practices, including LSA.
As suggested by many scholars, assessing reading comprehension in LSAs implies a number of challenges due to the complexity of the reading comprehension construct. For instance, important methodological issues are involved in the selection of stimulus-passages and interpretation of readers’ performance on reading assessments.
Within the INVALSI assessment, measures of LC from the LuCET project could provide further support in selecting passages that match students’ levels of reading comprehension. The INVALSI proficiency level descriptions could also benefit from including information about texts’ LC. INVALSI currently reports results in terms of proficiency levels, based on item descriptors. Including information about text features could provide a more nuanced description of what students are able to do at the end of high-school at different proficiency levels. This, in turn, could support the effectiveness of political stakeholders or educational practitioners with further substantial information on the proficiency status at both the system and subgroups levels.
It is important to note that data from the INVALSI National assessment play a pivotal role in the Italian National Recovery and Resilience Plan, Mission 4 ‘Education and Research’(Piano Nazionale di Ripresa e Resilienza, PNRR) approved by the Council of Ministers on 12 January 2021. Monitoring students’ achievements and basic skills, including reading comprehension, is of key relevance to reducing educational poverty and geographical disparities in terms of quantity and quality of education, thus ensuring equal opportunities for young people throughout the entire Italian territory and regardless of economic opportunities.
Further objective
By focusing on the last grade of secondary school, LuCET also aims at contributing to a further objective: to increase the proportion of the population aged 25 to 34 who have a tertiary degree. In LuCET, the population of students with atypical development was defined with this aim in mind, i.e., students who most probably will continue their studies at university, namely deaf students and students with Learning Difficulties. Providing effective measures of students’ achievements at the end of secondary school can foster secondary-tertiary transition (STT), which is considered of crucial importance for student retention and success in higher education.
Overall, procedures and models defined by the LuCET project could provide useful insights to the entire community working in the field of LSA, supporting shared knowledge and further developments in reading comprehension assessment.
LuCET research units will train young researchers who will receive research grants. They will develop relevant skills in the areas of data collection and analysis, and in their subsequent transfer in socially relevant application scenarios.