This dataset portrays the measurement of Digital Literacy of students based on the components Ability to use Media, Advanced use of Digital Media , Managing Digital Learning Platforms , and Ethics and Safety in the use of digital media in students at the junior high school level in the Madiun area in implementing and utilizing digital technology as a means as well as a source of material for learning. 385 respondents through online survei via Google Form.

This data set contains the replication data for the article "Knowing and doing: The development of information literacy measures to assess knowledge and practice." This article was published in the Journal of Information Literacy, in June 2021. The data was collected as part of the contact author's PhD research on information literacy (IL). One goal of this study is to assess students' levels of IL using three measures: 1) a 21-item IL test for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know. 2) a source-evaluation measure to assess students' abilities to critically evaluate information sources in practice. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. 3) a source-use measure to assess students' abilities to use sources correctly when writing. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. The data set contains survey results from 626 Norwegian and international students at three levels of higher education: bachelor, master's and PhD. The data was collected in Qualtrics from fall 2019 to spring 2020. In addition to the data set and this README file, two other files are available here: 1) test questions in the survey, including answer alternatives (IL_knowledge_tests.txt) 2) details of the assignment-based measures for assessing source evaluation and source use (Assignment_based_measures_assessing_IL_skills.txt) Publication abstract: This study touches upon three major themes in the field of information literacy (IL): the assessment of IL, the association between IL knowledge and skills, and the dimensionality of the IL construct. Three quantitative measures were developed and tested with several samples of university students to assess knowledge and skills for core facets of IL. These measures are freely available, applicable across disciplines, and easy to administer. Results indicate they are likely to be reliable and support valid interpretations. By measuring both knowledge and practice, the tools indicated low to moderate correlations between what students know about IL, and what they actually do when evaluating and using sources in authentic, graded assignments. The study is unique in using actual coursework to compare knowing and doing regarding students’ evaluation and use of sources. It provides one of the most thorough documentations of the development and testing of IL assessment measures to date. Results also urge us to ask whether the source-focused components of IL – information seeking, source evaluation and source use – can be considered unidimensional constructs or sets of disparate and more loosely related components, and findings support their heterogeneity.

This dataset is longitudinal in nature, comprising data from school years (2007/2008-2010/2011) following students in grade 1 to grade 4. Measures were chosen to provide a wide array of both reading and writing measures, encompassing reading and writing skills at the word, sentence, and larger passage or text levels. Participants were tested on all measures once a year, approximately one year apart. Participants were first grade students in the fall of 2007 whose parents consented to participate in the longitudinal study. Participants attended six different schools in a metropolitan school district in Tallahassee, Florida. Data was gathered by trained testers during thirty to sixty minute sessions in a quiet room designated for testing at the schools. The test battery was scored in a lab by two or more raters and discrepancies in the scoring were resolved by an additional rater.

Reading Measures Decoding Measures. The Woodcock Reading Mastery Tests-Revised (WRMT-R; Woodcock, 1987): Word Attack subtest was used to assess accuracy for decoding non-words. The Test of Word Reading Efficiency (TOWRE; Torgesen, Wagner, & Rashotte, 1999): Phonetic Decoding Efficiency (PDE) subtest was also used to assess pseudo-word reading fluency and accuracy. Both subtests were used to form a word level decoding latent factor. The WRMT-R Word Attack subtest consist of a list of non-words that are read out loud by the participant. The lists start off with letters and become increasingly more difficult to include complex non-words. Testing is discontinued after six consecutive incorrect items. The median reliability is reported to be .87 for Word Attack (Woodock, McGrew, & Mather, 2001). The TOWRE PDE requires accurately reading as many non-words as possible in 45 seconds. The TOWRE test manual reports test-retest reliability to be .90 for the PDE subtest. Sentence Reading Measures. Two forms of the Test of Silent Reading Efficiency and Comprehension (TOSREC, forms A and D; Wagner et al., 2010) were used as measures of silent reading fluency. Students were required to read brief statements (e.g., “a cow is an animal”) and verify the truthfulness of the statement by circling yes or no. Students are given three minutes to read and answer as many sentences as possible. The mean alternate forms reliability for the TOSREC ranges from .86 to .95.

Reading Comprehension Measures. The Woodcock-Johnson-III (WJ-III) Passage Comprehension subtest (Woodcock et al., 2001) and the Woodcock Reading Mastery TestRevised Passage Comprehension subtest (WRMT-R; Woodcock, 1987) were used to provide two indicators of reading comprehension. For both of the passage comprehension subtests, students read brief passages to identify missing words. Testing is discontinued when the ceiling is reached (six consecutive wrong answers or until the last page was reached). According to the test manuals, test-retest reliability is reported to be above .90 for WRMT-R, and the median reliability coefficient for WJ-III is reported to be .92.

Spelling Measures. The Spelling subtest from the Wide Range Achievement Test-3 (WRAT-3; Wilkinson, 1993) and the Spelling subtest from the Wechsler Individual Achievement Test-II (WIAT-II; The Psychological Corporation, 2002) were used to form a spelling factor. 14 Both spelling subtests required students to spell words with increasing difficulty from dictation. The ceiling for the WRAT3 Spelling subtest is misspelling ten consecutive words. If the first five words are not spelled correctly, the student is required to write his or her name and a series of letters and then continue spelling until they have missed ten consecutive items. The ceiling for WIAT-II is misspelling 6 consecutive words. The reliability of the WRAT-3 spelling subtest is reported to be .96 and the reliability of the WIAT-II Spelling subtest is reported to be .94.

Written Expression Measures. The Written Expression subtest from the Wechsler Individual Achievement Test-II (WIAT-II; The Psychological Corporation, 2002) was administered. Written Expression score is based on a composite of Word Fluency and Combining Sentences in first and second grades and a composite of Word Fluency, Combining Sentences, and Paragraph tasks in third grade. In this study the Combining Sentences task was used as an indicator of writing ability at the sentence level. For this task students are asked to combine various sentences into one meaningful sentence. According to the manual, the test-retest reliability coefficient for the Written Expression subtest is .86.

Writing Prompts. A writing composition task was also administered. Participants were asked to write a passage on a topic provided by the tester. Students were instructed to scratch out any mistakes and were not allowed to use erasers. The task was administered in groups and lasted 10 minutes. The passages for years 1 and 2 required expository writing and the passage for year 3 required narrative writing. The topics were as follows: choosing a pet for the classroom (year 1), favorite subject (year 2), a day off from school (year 3). The writing samples were transcribed into a computer database by two trained coders. In order to submit the samples to Coh-Metrix (described below) the coders also corrected the samples. Samples were corrected once for spelling and punctuation using a hard criterion (i.e., words were corrected individually for spelling errors regardless of the context, and run-on sentences were broken down into separate sentences). In addition, the samples were completely corrected using the soft criterion: corrections were made for spelling based on context (e.g., correcting there for their), punctuation, grammar, usage, and syntax (see Appendix A for examples of original and corrected transcripts). The samples that were corrected only for spelling and punctuation using the hard criterion were used for several reasons: (a) developing readers make many spelling errors which make their original samples illegible, and (b) the samples that were completely corrected do not stay true to the child’s writing ability. Accuracy of writing was not reflected in 15 the corrected samples because of the elimination of spelling errors. However, as mentioned above spelling ability was measured separately. Data on compositional fluency and complexity were obtained from Coh-Metrix. Compositional fluency refers to how much writing was done and complexity refers to the density of writing and length of sentences (Berninger et al., 2002; Wagner et al., 2010).

Coh-Metrix Measures. The transcribed samples were analyzed using Coh-Metrix (McNamara et al., 2005; Graesser et al., 2004). Coh-Metrix is a computer scoring system that analyzes over 50 measures of coherence, cohesion, language, and readability of texts. Appendix B contains the list of variables provided by Coh-Metrix. In the present study, the variables were broadly grouped into the following categories: a) syntactic, b) semantic, c) compositional fluency, d) frequency, e) readability and f) situation model. Syntactic measures provide information on pronouns, noun phrases, verb and noun constituents, connectives, type-token ratio, and number of words before the main verb. Connectives are words such as so and because that are used to connect clauses. Causal, logical, additive and temporal connectives indicate cohesion and logical ordering of ideas. Type-token ratio is the ratio of unique words to the number of times each word is used. Semantic measures provide information on nouns, word stems, anaphors, content word overlap, Latent Semantic Analysis (LSA), concreteness, and hypernyms. Anaphors are words (such as pronouns) used to avoid repetition (e.g., she refers to a person that was previously described in the text). LSA refers to how conceptually similar each sentence is to every other sentence in the text. Concreteness refers to the level of imaginability of a word, or the extent to which words are not abstract. Concrete words have more distinctive features and can be easily pictured in the mind. Hypernym is also a measure of concreteness and refers to the conceptual taxonomic level of a word (for example, chair has 7 hypernym levels: seat -> furniture -> furnishings -> instrumentality -> artifact -> object -> entity). Compositional fluency measures include the number of paragraphs, sentences and words, as well as their average length and the frequencies of content words. Frequency indices provide information on the frequency of content words, including several transformations of the raw frequency score. Content words are nouns, adverbs, adjectives, main verbs, and other categories with rich conceptual content. Readability indices are related to fluency and include two traditional indices used to assess difficulty of text: Flesch Reading Ease Score and Flesch- 16 Kincaid Grade Level. Finally, situation model indices describe what the text is about, including causality of events and actions, intentionality of performing actions, tenses of actions and spatial information. Because Coh-Metrix hasn’t been widely used to study the development of writing in primary grade children (Puranik et al., 2010) the variables used in the present study were determined in an exploratory manner described below. Out of the 56 variables, 3 were used in the present study: total number of words, total number of sentences and average sentence length (or average number of words per sentence). Nelson and Van Meter (2007) report that total word productivity is a robust measure of developmental growth in writing. Therefore, indicators for a paragraph level factor included total number of words and total number of sentences. Average words per sentence was used as an indicator for a latent sentence level factor, along with the WIAT-II Combining Sentences task.

Following the Sunshine State Standards, students are required to take the Florida

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2022, the degree of literacy in India was about 76.32 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries. The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

This data represents the outputs and outcomes of the City funded digital literacy training and public access computer lab contract (Community Technology Access Lab Management & Digital Literacy Skills Training Services contract). This data shows the number of clients served and the percent of digital literacy training clients who increase their digital skill as well as data showing usage and availability of computer labs. Data is reported by contractors quarterly via a grant management system (PartnerGrants) and then transferred to this reporting format. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/muck-3gny

This dataset has been deprecated December 2023 and has been replaced with a new dataset that supports the 3.33 Culture of Literacy and Engagement performance measure.This data includes literacy rates based on participation numbers for the Maricopa County Library District Summer Reading Program, Tempe Public Library Story Time engagement, Tempe Public Library materials use and use of electronic resources at Tempe Public Library.This page provides data for the Culture of Literacy and Engagement performance measure.The performance measure dashboard is available at 3.33 Culture of Literacy and Engagement.Additional InformationSource: Contact: Kathy HusserContact E-Mail: Kathy_Husser@tempe.govData Source Type: ExcelPreparation Method: Department-generated reports from Polaris and from the Maricopa County Library District; Use of TPL materials (physical print, audio and video borrowed) per Capita. Use of TPL cmputr sessions equals the total number of public computers utilized during open hours vs. the current Tempe population.Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries. The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

This repository hosts the The Guardian Reading Dataset, designed to explore readers' experiences with textual complexity, comprehensibility, and interest. The dataset captures detailed subjective and objective measures of readers' interactions with a selection of articles from The Guardian, providing granular insights into how textual features impact reading engagement.DescriptionThis dataset includes data from 30 readers who participated in 540 reading sessions. Each participant evaluated 18 articles sampled at three levels of textual complexity (low, medium, high), determined by a readability algorithm (Van der Sluis, 2014). The data captures subjective appraisals of complexity, comprehensibility, and interest, alongside eye-tracking metrics to provide an objective view of readers' processing difficulty and engagement with the text.Data File and StructureData is stored in .csv format at the reading session level, with each row corresponding to a unique reading session by a participant. Participant identifiers, demographic scales, and trait measures were dropped as part of anynomisation. Key columns are:stimulus: Identifier for each article.appraised_complexity: Participant’s perceived complexity of the article.appraised_comprehensibility: Participant’s perceived comprehensibility of the article.processing_fluency: Combined score of appraised complexity and appraised comprehensibility.interest: Participant’s interest rating for the article.topical_familiarity: Participant’s familiarity with the article’s topic.familiarity_group:Grouping of articles into balanced blocks (either low, median, or high familiarity).pupil_diameter: Average pupil diameter across the reading session, reflecting cognitive load during reading.pupil_corrected: Baseline-corrected pupil diameter per reading session (details on preprocessing and correction can be found in Van der Sluis et al., 2023).novelty_comprehensibility_scale_*: Semantic differentials, rating the content as complex – simple (57), not familiar to me – very familiar to me, easy to read – difficult to read(59), easy to understand – hard to understand (60), comprehensible – incomprehensible (61), coherent – incoherent (62), interesting – uninteresting (63), boring – exciting (64). Of these, 59, 60, 61, 62, and 63 were reverse scored in the resulting scales.fam_answer_*: Participants' topical familiarity rating for five topics per article. Note that each column covers different topics depending on the article.Data is stored in .db format at the stimulus (text) level, with each row corresponding to a unique text. In addition to averaged measurements aggregated per article, key columns are:text: Article content excerpt (first 50 and last 50 words of excerpt presented to study participants).url: Source URL for full text access.PurposeThe purpose of this dataset is to facilitate the analysis of human responses to differences in textual complexity, with a focus on understanding how readers' interest varies with different complexity levels. The controlled conditions and validated data in this dataset make it ideal for assessing the accuracy and applicability of models of textual complexity, ensuring that the findings are both reliable and relevant to actual readers' perceptions and experiences.LicensingThe dataset contains textual excerpts and metadata from The Guardian, shared under The Guardian’s open license terms (https:/www.theguardian.com/info/2022/nov/01/open-licence-terms). Full-text sharing is restricted, but excerpts of up till 100 words may be used with proper attribution.ReferencesVan der Sluis, F., & van den Broek, E. L. (2023). Feedback beyond accuracy: Using eye-tracking to detect comprehensibility and interest during reading. Journal of the Association for Information Science and Technology, 74(1): 3–16. https://doi.org/10.1002/asi.24657Van der Sluis, F., van den Broek, E. L., Glassey, R. J., van Dijk, E. M. A. G., & de Jong, F. M. G. (2014). When complexity becomes interesting. Journal of the American Society for Information Science and Technology, 65(7): 1478–1500. https://doi.org/10.1002/asi.23095

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/Nm4G3Z3VaURgig9V3BTfg

Reading literacy focuses on the ability of students to use written information in situations which they encounter in their life. The data are coming from the Programme for International Student Assessment (PISA) which is an internationally standardised assessment that was developed by the OECD and administered to 15-year-olds in schools. In PISA, reading literacy is defined as understanding, using and reflecting written texts, in order to achieve one's goals, to develop one's knowledge and potential and to participate in society. Proficiency at Level 1 and below means that the pupils are not likely to demonstrate success on the most basic type of reading that PISA seeks to measure. Such students have serious difficulties in using reading literacy as an effective tool to advance and extend their knowledge and skills in other areas.

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries. The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Headteachers from local schools were invited to take part in the study. We then asked the school leadership to give their approval for us to conduct the research in their school. Following approval, parents were informed about the study, and given an opportunity to opt their child out of the assessments. Children attending participating schools, whose parents have not opted out of the study, were then invited to participate and given information about the study in age-appropriate terms, and asked them whether they want to take part. We only tested children who actively consented to take part and it was made clear that they could stop at any time. All children from the appropriate year group (Reception, Year 1 or Year 2) were invited to take part. No child was excluded unless their parents opted them out or if the child did not actively consent. Children who had difficulty understanding/ communicating were allowed to participate to the best of their ability and praised for their participation, as normal. The tests were designed for use with children at all levels of ability and verbal instructions were kept to a minimum. The assessments were conducted on a one-to-one basis in a quiet area close to the child’s classroom. Research Assistants who were experienced in working with children conducted the assessments. Research Assistants were trained to work with each child on a one-to-one basis, using the exact same verbal instructions and procedure for each child, praising the child for their efforts, regardless of their performance. Each phase of data collection was conducted over a three month period, requiring up to 6 sessions of approximately 20mins per child. The assessments we used were a combination of standardised tests of reading, memory and nonverbal reasoning plus bespoke measures. These measures are described in Cunningham et al. (2015). Children responded either by articulating the speech sounds, or through key presses which enabled them to repeat back the sounds. Laptops were used to present the stimuli through headphones and allow responses to be directly recorded onto a spreadsheet. In order to avoid causing discomfort, the tests were designed so that the initial material was easy, progressing to more difficult material, and tests that include a large amount of difficult material were discontinued once the child made a certain number of errors (see Cunningham, et al., 2015).

Data sets and syntax files related to published article:
Hoek, R. W., Rozendaal, E., Van Schie, H. T., Van Reijmersdal, E. A., & Buijzen, M. (2020). Testing the Effectiveness of a Disclosure in Activating Children’s Advertising Literacy in the Context of Embedded Advertising in Vlogs. Front. Psychol. 11:451. doi: 10.3389/fpsyg.2020.00451

Watching vlogs of social media influencers has become a favorite pastime for children and adolescents. For advertisers, vlogs are an excellent way to reach young viewers. As such, vlogs have become a powerful marketing tool. However, for children and adolescents it is often unclear whether a vlog contains advertising, which raises questions regarding the fairness and transparency of this type of advertising. If children do not recognize the commercial intent of in-vlog advertising, then they are unlikely to activate their advertising literacy, which may serve as a critical coping mechanism. The aim of this study was to investigate if a sponsorship disclosure stimulates children and adolescents’ (7–16 years old) to activate their advertising literacy when exposed to embedded advertising in vlogs and, subsequently, if advertising literacy activation is related to children’s brand attitude. Furthermore, we investigated whether the relation between exposure to a sponsorship disclosure and advertising literacy activation was moderated by children’s dispositional advertising literacy and their age. An innovative aspect of the current study is that advertising literacy activation was measured in two ways: with a self-reported questionnaire and via an indirect measurement task (Advertising Literacy Activation Task). The results showed that the children who were exposed to a sponsorship disclosure did not activate their advertising literacy to a higher extent than the children who were not exposed to such a disclosure. This might be because of the high prominence of the brand in the vlog; thus children may not have needed the disclosure to realize that the vlog was sponsored and accordingly activate their advertising literacy. The results also showed that stronger attitudinal advertising literacy activation led to a more negative brand attitude. Interestingly, this effect was only found when attitudinal advertising literacy was assessed with a questionnaire and not when it was assessed with the indirect measurement task. Thus, children who were more critical toward the in-vlog advertisement through self-reporting also had a more negative brand attitude. This suggests that direct and indirect measurements of advertising literacy activation reveal different processes through which children make sense of, and are affected by, advertising.

This dataset contains data from a 12-question survey designed to measure the level of climate literacy among the adult population of Quebec (Canada). Data collection took place between September 17, 2024 and October 12, 2024. Two thousand five hundred and thirteen (2513) respondents aged 18 years or older completed the survey. Sociodemographic questions about age, sex, first language, education, region of residence and presence of children in the household allowed for the creation of post-stratification weights to have a representative sample of the adult population in the province of Quebec.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The survey covers the urban area of two largest cities of Vietnam, Ha Noi and HCMCT.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The STEP target population is the population aged 15 to 64 included, living in urban areas, as defined by each country's statistical office. In Vietnam, the target population comprised all people from 15-64 years old living in urban areas in Ha Noi and Ho Chi Minh City (HCM).

The reasons for selection of these two cities include :

(i) They are two biggest cities of Vietnam, so they would have all urban characteristics needed for STEP study, and (ii) It is less costly to conduct STEP survey in these to cities, compared to all urban areas of Vietnam, given limitation of survey budget.

• The target population is not representative for the national urban population.

The following are excluded from the sample:

• Residents of institutions (prisons, hospitals, etc)
• Residents of senior homes and hospices
• Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc
• Persons living outside the country at the time of data collection

Kind of data

Sample survey data [ssd]

Sampling procedure

• The sample of 3405 households was selected from 227 urban Enumeration Areas (EAs) in Ha Noi (107 EAs) and Ho Chi Minh City (120 EAs). From each EA 15 households were selected, so the number of households selected in Ha Noi was 1245 HHs, and in HCM, 2160 HHs.
• The 2009 Population and Housing Census was used as a sample frame.
• Regarding PSUs (EAs), the sampling frame is the list of 15% of total EAs of the 2009 Population Census. Data items on the frame for PSU include provincecode, districtcode, commune code, and EA code; address of EA, number of households.
• Regarding ultimate sampling units (households), sampling frame is a list of (100) households in each EA. Data items on the frame for ultimate sampling units (households) include names of heads of households.

The sample frame includes the list of urban EAs and the count of households for each EA. Changes of the EAs list and household list would impact on coverage of sample frame. In a recent review of Ha Noi, there were only 3 EAs either new or destroyed from 140 randomly selected Eas (2%). GSO would increase the coverage of sample frame (>95% as standard) by updating the household list of the selected Eas before selecting households for STEP.

A detailed description of the sample design is available in section 4 of the NSDPR provided with the metadata. On completion of the household listing operation, GSO will deliver to the World Bank a copy of the lists, and an Excel spreadsheet with the total number of households listed in each of the 227 visited PSUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) a Background Questionnaire developed by the WB STEP team (ii) a Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. The WB STEP team and ETS collaborated closely with the survey firms during the process and reviewed the adaptation and translation to Vietnamese (using a back translation). - The survey instruments were both piloted as part of the survey pretest. - The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process 1. Raw data is sent by the survey firm 2. The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6. ETS scales the Reading Literacy Assessment data. 7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

The response rate for Vietnam (urban) was 62%. (See STEP Methodology Note Table 4).

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. All country weighting documentations are provided as an external resource.

The dataset contains all raw data used for analyses in the research of Braams and Bosman (2000), and Study 3 described by Walda, et al. (in preparation). The manuscript of Braams and Bosman addressed kindergarten predictors of reading and spelling level. The manuscript of Walda et al. (in preparation) addressed the identification of determinants of dyslexia by traditional, linear techniques as well as machine learning techniques. For a full description of all measures see the README file. The experiment design was a longitudinal repeated measures design. During three moments of measurement tests were administered in an one-to-one assessment setting.The dataset includes test scores for phonological awareness and initial word decoding. Phonological awareness was assessed once: In the last year of Kindergarten (T1). Word decoding was assessed twice: Halfway Grade 1 (T2), and at the end of Grade 1(T3).

The dataset contains all raw data used for analyses in the research of Study 2 described by Walda et al. (under review). The manuscript addressed the identification of determinants of dyslexia by traditional, linear techniques as well as machine learning techniques. For a full description of the reading and spelling remediation program, and all measures see the README file. The experiment design was a longitudinal repeated measures design. During two moments of measurement tests were administered in an one-to-one assessment setting. The pretest (T1) took place prior to all interventions, and the follow-up measurements was administered after three months (T2) of reading and spelling remediation. In this document, the process of collecting data is described, followed by an overview and description of variables included in the data set.
The data set is based on an earlier version, used for analyses in the research of Walda, van Weerdenburg, Wijnants, and Bosman (2015). The earlier version of the dataset will be published and linked. In the present dataset, both variables and cases have been added. However, the present data set includes only two moments of measurement, instead of four.
The dataset includes test scores for word decoding, grapheme-phoneme identification, grapheme-phoneme discrimination, naming speed, vocabulary, nonverbal reasoning, digit recall, block recall, and word recall at T1 in children with severe dyslexia prior to a reading and spelling remediation program in a specialized reading clinic in the Netherlands. The dataset includes test scores for word decoding at T2, after three months of reading and spelling remediation.

Diverse learning theories have been constructed to understand learners' internal states through various tangible predictors. We focus on self-regulatory actions that are subconscious and habitual actions triggered by behavior agents' 'awareness' of their attention loss. We hypothesize that self-regulatory behaviors (i.e., attention regulation behaviors) also occur in e-reading as 'regulators' as found in other behavior models (Ekman, P., & Friesen, W. V., 1969). In this work, we try to define the types and frequencies of attention regulation behaviors in e-reading. We collected various cues that reflect learners' moment-to-moment and page-to-page cognitive states to understand the learners' attention in e-reading.

The text 'How to make the most of your day at Disneyland Resort Paris' has been implemented on a screen-based e-reader, which we developed in a pdf-reader format. An informative, entertaining text was adopted to capture learners' attentional shifts during knowledge acquisition. The text has 2685 words, distributed over ten pages, with one subtopic on each page. A built-in webcam on Mac Pro and a mouse have been used for the data collection, aiming for real-world implementation only with essential computational devices. A height-adjustable laptop stand has been used to compensate for participants' eye levels.

Thirty learners in higher education have been invited for a screen-based e-reading task (M=16.2, SD=5.2 minutes). A pre-test questionnaire with ten multiple-choice questions was given before the reading to check their prior knowledge level about the topic. There was no specific time limit to finish the questionnaire. We collected cues that reflect learners' moment-to-moment and page-to-page cognitive states to understand the learners' attention in e-reading. Learners were asked to report their distractions on two levels during the reading: 1) In-text distraction (e.g., still reading the text with low attentiveness) or 2) out-of-text distraction (e.g., thinking of something else while not reading the text anymore). We implemented two noticeably-designed buttons on the right-hand side of the screen interface to minimize possible distraction from the reporting task. After triggering a new page, we implemented blur stimuli on the text in the random range of 20 seconds. It ensures that the blur stimuli occur at least once on each page. Participants were asked to click the de-blur button on the text area of the screen to proceed with the reading. The button has been implemented in the whole text area, so participants can minimize the effort to find and click the button. Reaction time for de-blur has been measured, too, to grasp the arousal of learners during the reading. We asked participants to answer pre-test and post-test questionnaires about the reading material. Participants were given ten multiple-choice questions before the session, while the same set of questions was given after the reading session (i.e., formative questions) with added subtopic summarization questions (i.e., summative questions). It can provide insights into the quantitative and qualitative knowledge gained through the session and different learning outcomes based on individual differences. A video dataset of 931,440 frames has been annotated with the attention regulator behaviors using an annotation tool that plays the long sequence clip by clip, which contains 30 frames. Two annotators (doctoral students) have done two stages of labeling. In the first stage, the annotators were trained on the labeling criteria and annotated the attention regulator behaviors separately based on their judgments. The labels were summarized and cross-checked in the second round to address the inconsistent cases, resulting in five attention regulation behaviors and one neutral state. See WEDAR_readme.csv for detailed descriptions of features.

The dataset has been uploaded 1) raw data, which has formed as we collected, and 2) preprocessed, that we extracted useful features for further learning analytics based on real-time and post-hoc data.

Reference

Ekman, P., & Friesen, W. V. (1969). The repertoire of nonverbal behavior: Categories, origins, usage, and coding. semiotica, 1(1), 49-98.