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Numerous researchers have developed technological pedagogical content knowledge (TPACK) instruments for English language teaching; however, a valid and reliable instrument to unbox the primary English language teacher's TPACK profile from graduates of the primary school teacher education study program does not yet exist. Consequently, this study aimed to design and validate a questionnaire to unbox the primary English language teacher's TPACK profile. This study used a mixed-methods approach. First, qualitative methods were employed to verify and validate the content of each construct. The items were generated through experts' and a primary English teacher's reviews. As a quantitative method, the questionnaire was then validated through explanatory factor analysis (EFA). The result of EFA yielded a comprehensive set of 40 items: technological knowledge (TK) section (consisting of 5 items), content knowledge (CK) section (consisting of 6 items), pedagogical knowledge (PK) section (consisting of 6 items), pedagogical content knowledge (PCK) section (consisting of 5 items), technological content knowledge (TCK) section (consisting of 5 items), technological pedagogical knowledge (TPK) section (consisting of 7 items), and TPACK section (consisting of 5 items). The reliability coefficients of each construct were acceptable. In conclusion, the study obtained a fruitful questionnaire for unboxing the primary English language teacher's TPACK profile.

The circumstances associated with high levels of achievement in undergraduate studies has not been thoroughly explored in the Australian context. This study investigated factors predicting high academic achievement, defined as maintaining a Distinction average, at undergraduate level. Findings revealed several factors that predicted achievement after two semesters of study. These factors included essay writing skills, with students having satisfactory writing 5.16 times more likely to maintain a Distinction average compared to those with below satisfactory writing; faculty, with students enrolled in Health Sciences 4.63 times more likely compared to students from other faculties; language background, with English-speaking background students 1.67 times more likely compared to English as an additional language or dialect (EAL/D) students; gender, with females 1.45 more likely compared to males; and age, with older students 1.03 times more likely to achieve high performance compared to younger students. Creating a profile of students likely to excel academically assists decision makers in allocating resources to students less likely to achieve. This research opens the door to further studies investigating whether these factors play a role in predicting student achievement at university.

The researchers examined the relationship between informal digital gameplay and learner profiles in two Japanese private universities. Seventy-eight participants responded to 59 items on a digital questionnaire that consisted of four sections: 1) Learner Profiles, 2) Digital Game Usage, 3) Open-Responses, and 4) Demographics. The results of the study demonstrated that students positively identified with the learner profiles featured on the questionnaire (M = 3.80). The highest mean values were associated with the profiles of Digital Citizen (M = 4.30) and Knowledge Constructor (M = 4.15). It was discovered that participants engaged in digital gameplay most often with smartphones (M = 3.64) and game consoles (M = 2.79). Simulation (M = 2.81) and real-time strategy (M = 2.66) games were the most frequently played. The results of a Pearson Product Moment Correlation showed that there was no significant relationship between informal digital gameplay and learner profiles. Furthermore, self-identified gamers and non-gamers did not significantly differ on the Learner Profile Total Scale or subscales.

As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners' behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners' profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners' micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.

Large-scale assessments play a key role in education: educators and stakeholders need to know what students know and can do, so that they can be prepared for education policies and interventions in teaching and learning. However, a score from the assessment may not be enough--educators need to know why students got low scores, how students engaged with the tasks and the assessment, and how students with different levels of skills worked through the assessment. Process data, combined with response data, reflect students' test-taking processes and can provide educators such rich information, but manually labelling the complex data is hard to scale for large-scale assessments. From scratch, we leveraged machine learning techniques (including supervised, unsupervised, and active learning) and experimented with a general human-centred AI approach to help subject matter experts efficiently and effectively make sense of big data (including students' interaction sequences with the digital assessment platform, such as response, timing, and tool use sequences) to provide process profiles, that is, a holistic view of students' entire test-taking processes on the assessment, so that performance can be viewed in context. Process profiles may help identify different sources for low performance and help generate rich feedback to educators and policy makers. The released National Assessment of Educational Progress (NAEP) Grade 8 mathematics data were used to illustrate our proposed approach.

Systematic overview of environmental literacy research in Indonesia is a crucial aspect for determining effective strategies for strengthening environmental literacy, unfortunately, this is not widely available, especially regarding research published in Indonesia. This research aims to reveal the profile, challenges, and opportunities for strengthening environmental literacy in Indonesia. SLR was used in this research by analyzing 36 articles that met the requirements collected from journals registered by Sinta in 10 years. The articles were then analyzed using a content analysis observation sheet. This research showed fluctuations and stagnation in the number of publications. Variations in design, subjects, instruments, and data analysis techniques are also revealed. Important research results also provide interesting findings in environmental literacy profiles, types of schools, and educational curricula. Based on this, researchers in Indonesia have the challenge of increasing the number of environmental literacy research published in Indonesia. This challenge becomes an opportunity to conduct qualitative and mixed-method research involving students at various education levels. Triangulation data analysis is also important. Furthermore, implementing the Adiwiyata curriculum and innovative learning using local wisdom has the potential to strengthen students' environmental literacy. This research provides an overview for other researchers to formulate effective strategies to improve environmental literacy.

This study aims to reconstruct Grade 11 students' misconceptions about force through the Conceptual Change Model (CCM) making use of Predict, Discuss, Explain, Observe, Discuss, Explore, Explain (PDEODEE) tasks. This study was conducted using a mixedmethod approach. The participants in this study were 65 students (33 students in the experiment class--13 males and 20 females, and 32 students in the control class--12 males and 20 females) from a high school in Bandung, Indonesia. The instrument test used is the Four-Tier Test-formed Force Concept Inventory (4TT-FCI) with 8 question items. Three analyses were carried out: an analysis of students' misconception profiles during the pre-test and post-test, the reconstruction of students' misconceptions analysed based on percentage and codification using Great Change (GC), Not Change (NC), and Un-Great Change (U-GC). The improvement in students' conception was carried out using N-Gain with three categories (Low, Moderate, and High). The results show that the students' conception profile during the pre-test and post-test still detects misconceptions. However, these misconceptions can be ameliorated using CCM with PDEODEE tasks because the highest conceptual change is in the Misconceptions category (GC= 34.5) and the lowest is in the Un-Code category (U-GC= 2.4). The increase in the correctness of the conception was measured in the experimental class at 0.73 (High category), and the control class is 0.42 (Moderate Category).

E-learning platforms have become increasingly popular in K--8 education to promote student learning and enhance classroom teaching. Student interactions with these platforms produce trace data, which are digital records of learning processes. Although trace data have been effective in identifying learners' engagement profiles in higher education and lifelong learning (e.g., MOOCs), similar research on young learners has been very scarce. This study makes a timely and novel contribution to the field by identifying emerging profiles of young students from Grades 1 to 8 based on their engagement in an e-learning platform. The k-means cluster analysis yielded seven distinct profiles in total. While the dominance of profiles differed across grade levels, some profiles were not even present at specific grades, indicating the changing engagement behaviours of young learners in different years. In addition, all emerging profiles suggest students tended to focus on specific components of the platform, thus lacking balanced engagement with all resources. In particular, while primary school students showed relatively higher interest in game-based learning, middle school students focused more on exams than their counterparts. The findings yielded seven specific considerations for the design of effective e-learning platforms.

The purpose of this study is to identify the difference between elementary pupils' higher order thinking skill (HOTS) in an environment learning using a variety of resources in a local culture-based learning. HOTS differences between those who use printed and electronic learning resources after controlling self-regulated learning pupil. This is an experimental quasi-research. Four groups of elementary school pupils from Ngada, Nusa Tenggara Timur, Indonesia, are the subject of the study. A three-dimensional self-reliance feature and 20 item of variable indicator descriptions are used to collect data, as is a high-level cognitive ability exam at the level of analysis, assessment, and invention of 10 item. To test the research hypothesis, the data was then examined using covariate analysis. A qualitative and descriptive analysis follows. A descriptive analysis is used to characterize the student's (high-level thinking) and deviation standards, whilst a qualitative analysis is used to explain high levels of thought profiles on elementary school thematic study. The results show a significant difference between students who are participating in incubation studies that draw on printed study resources and electronic study sources in terms of their high level of thinking capacity. Once the free learning variable was taken into account, the outcome indicated substance differences.

It is characteristic that besides other duties teacher competence management and development is becoming a part of school responsibilities. Still, school leaders lack the experience and instruments to accomplish these duties. From a teacher's perspective competence management and development can be seen as the process of identification and implementation of professional development (PD). An effective competence management approach that is widely used in business environments is the identification of required and actual competence profiles to judge which development is needed. Such a competence management approach isn't characteristic of school environments, still a promising perspective on how to solve challenges regarding teacher PD (TPD). TPD interventions are often criticized as being too general ("one size fits all" dominates); therefore, the search for practices on how to "tailor" PD initiatives to individual teacher needs is topical for TPD research. An effective way to personalize TPD could be the determination of TPD profiles and the design of the PD around these profiles. Person-centered approaches dominate in the identification of science teacher profiles, as surveys and tests are commonly used. Examples, of how the identification of science teacher profiles can be done using lesson observation data are missing. Science teaching is a complex process; to limit the complexity of this study, the authors focus on teaching that promotes student conceptual understanding (CU). A mixed method study was designed and conducted in a sample of 26 science teachers, who represented urban municipalities' science teacher population. The study included science teacher lesson observation and analysis, and determination of science teacher performance regarded teaching that promotes student CU. Science teacher performance data were used to identify teachers with similar performance across the selected criteria and to create science teacher profiles. A methodology for how lesson observation data can be used to identify teacher profiles in small teacher samples is described. Six various science teacher profiles in teaching that promote student understanding were identified, characterizing the variety of science TPD needs.

The dual-factor model of mental health proposes that high wellbeing and low distress are necessary to define mental health. This study used latent profile analysis to identify mental health profiles in a sample of 3,587 Australian grade 6 students and explored the association between mental health profiles and school outcomes measured in grades 7 and 9. Six mental health profiles were identified: complete mental health (i.e. high wellbeing and low distress; 30%), moderately mentally healthy (i.e. average wellbeing and low distress; 18%), symptomatic but content (i.e. high wellbeing and above-average distress; 19%), vulnerable (i.e. low wellbeing and below-average distress; 6%), moderately troubled (i.e. below-average wellbeing and high distress; 19%), and troubled (i.e. low wellbeing and high distress; 8%). After statistical adjustment for potential confounders (gender, language background, socio-economic status, and geographical remoteness), students with complete mental health showed significantly higher academic achievement and school engagement one and three years later compared with students with all other mental health profiles. Students with vulnerable and troubled profiles experienced the poorest school outcomes sustained over time (grade 7 and grade 9). Implications for school-based interventions to support the mental health and wellbeing of students are discussed.

A person-centered approach identified five empirically unobserved student profiles of first-time full-time university students who shared similar academic patterns, performance measures, and demographic characteristics. "Honors" and "Flourishing" classes tended to graduate within 6 years of attempting their first-college mathematics course, earned a high grade in that course, and made continued degree progress. The "Flourishing" class had lower semester and first-college mathematics GPAs than the "Honors" class but higher GPAs than the "Tenacious" class. Placement in remedial mathematics alone did not determine class membership. The "Honors" class had an overrepresentation of White women, and an underrepresentation of Black men. Hispanic students were most prevalent in the "Flourishing" group while Black men were more prevalent in the "Tenacious" group. As a whole, men fared worse on outcomes than women.

Background: This study explored the impact of different cut-off points used to identify children with reading difficulties on the distribution of these children across the three poor reader groups predicted by the simple view of reading (dyslexia, specific comprehension difficulty and mixed difficulty). Additionally, the study investigated whether the cognitive profiles of these poor reader groups remained consistent across varying levels of reading impairment. Methods: This study included 209 primary school children from New Zealand, in Years 4-6 and aged 8-10 years, who experienced reading comprehension difficulties. Using a two-step cluster analysis, participants were assigned to one of three poor reader groups: dyslexia, specific comprehension difficulty (SCD) and mixed difficulty. We examined the distribution of children across these groups at eight levels of reading comprehension difficulty and conducted strengths and weaknesses profiles at four levels of reading ability across 14 tests that assessed a range of reading-related skills. Additionally, we compared the performance of children in these groups who performed above and below the 10th percentile on a reading comprehension assessment across these tests. Results: The results showed that the proportion of children assigned to each group varied across the reading levels, with children at the lower end of the reading comprehension continuum more likely to exhibit the mixed difficulty profile than the SCD and dyslexia profiles. Overall, the research found that dyslexia, SCD and mixed difficulty profiles could be identified at all levels of reading comprehension difficulty, though weaknesses in their reading-related skills increased as reading comprehension difficulties also increased. Conclusions: Our findings suggest that struggling readers demonstrate distinct cognitive profiles across all levels of reading ability. These results have important implications for identifying and supporting struggling readers, as well as enhancing our understanding of the underlying mechanisms of reading difficulties. The possible relationship between the results obtained in this study and tiered models of reading support is discussed.

This paper explores the possible relationships between students' positive attitudes and interest toward technology and teachers' approaches. The study was conducted with a sample of students (N = 2312) from 5 to 8th grade of primary school in Croatia. Reduced descriptions of teachers' approaches were used as predictors of students' positive attitudes and interest. Multiple regression was used to determine correlations between teachers' approaches and students' attitudes. The results show a weak but positive correlation between students' attitudes and interests and teachers' approaches typical of the flexible teacher profile. This refers to approaches in which teachers ensure that they understand the purpose of learning, implement activities that are useful from the students' perspective, and allow students to do what they are successful at. Although the characteristics of Croatian technology teachers fit the flexible teacher profile, the correlated approaches identified here do not dominate. Despite the results, due to the complexity of the influence on students' interests and attitudes, further research is needed that considers other possible predictors and their interactions.

This study aims to classify student profiles based on the type and frequency of Information and Communication Technologies (ICT) usage. Each profile exhibits homogeneous characteristics and heterogeneous characteristics compared to other groups. Additionally, the study investigates whether covariates at the school and student levels create differences in these profiles. For this purpose, a mixed-method analysis (Latent Profile Analysis (LPA), Multi-level Latent Profile Analysis (MLPA) and Multi-level Logistic Regression Analysis (MLRA)) were used to identify ICT usage profiles. The study analysed data from 6,890 Turkish students across 186 schools participating in PISA 2018 (Program for International Student Assessment), revealing three distinct ICT usage profiles: Profile-1 (Low-Level ICT Users (19.06%)), Profile-2 (Intermediate-Level ICT Users (71.10%)), and Profile-3 (Advanced-Level ICT Users (9.84%)). The MLRA showed that students with low ICT usage have less access to ICT resources. Students in the Low-Level ICT Users profile also had lower scores on ICT-related motivation factors (interest, competence, social interaction, and autonomy). No significant effect of ICT usage on mathematics, reading, and science scores was observed. Variance differences at student and school levels could not be fully explained by covariates at the school level (type of school, ratio of computers connected to the internet per student, lack of educational materials and personnel). However, the ratio of computers per student effectively explained these differences. According to an MLPA, schools were categorised into two groups based on ICT resources: 'high' and 'low'. In schools with high ICT resources, the proportion of students in Profile-2 was found to be 45.8%, while those in Profile-3 accounted for 9%. In contrast, in schools with low ICT resources, the percentage of students in Profile-2 was 22.45%, and in Profile-3, it was 1.82%. These findings indicate that the level of a school's ICT resources significantly impacts students' technology usage skills. A significant finding of the research is that most Turkish students use ICT at an intermediate level at home for school lessons. The COVID-19 pandemic has further increased the importance of studying at home using ICT resources.

Background: Postdoctoral training holds an increasingly important place in preparation for leading academic and research positions. While little empirical research has described postdoctoral training beyond the sciences, across all fields, "misaligned expectations" are often touted as a key source of postdoctoral strife. Purpose/Hypothesis: This article describes mentorship competency beliefs within engineering and computer science fields, which increasingly engage in postdoctoral training. Design/Method: An embedded mixed-methods design was used to quantitatively identify mentorship profiles from survey data using latent profile analysis (LPA) from a sample of n = 118 postdoctoral scholars and n = 165 postdoctoral supervisors. Qualitative thematic analysis of interviews with n = 29 postdoctoral scholars and n = 20 postdoctoral supervisors was used to identify meaning in the differences between quantitative profiles. The combination of LPA with thematic analysis enabled the triangulation of distinct postdoctoral mentorship profile definitions. Results: LPA identified six postdoctoral fellow profiles and four supervisor profiles, which became clearly definable through thematic analysis. Postdoc profiles included Technical Manager, Autonomy Focused Advisor, Stretched Mentor, Well-Rounded Mentor, Exemplar Mentor, and Leader-Mentor, while supervisor profiles included Autonomous Mentor, Reflective Mentor, Research Lab Mentor, and Confident Leader-Mentor. Some of these are aligned, but several are not, giving insight into the phenomenon of "misaligned expectations" in postdoctoral literature. Conclusions: The mentorship profiles illustrate the misalignment in expectations, which leads to negative mentorship experiences for many postdoctoral scholars.

Background: The working memory (WM) system is recognized as a crucial cognitive function that underpins the acquisition of new knowledge and the development of foundational skills during childhood. Children's early literacy and numeracy skills lay the foundation for future academic success in reading and mathematics. While previous research has established a link between WM and early literacy as well as numeracy, the specific contributions of different WM components to the development of various skills in kindergarten-aged children remain unclear. Objective: This study aimed to investigate the associations between distinct profiles of WM and academic achievements in early literacy and numeracy among kindergarten children. Method: A battery of memory tests (simple WM and complex WM) were administered to a cohort of 250 kindergarten children aged between five and seven years. Additionally, a range of tasks assessing mathematical and language skills were administered. Results: Our findings align with our initial hypotheses, revealing differences between profiles of simple WM and complex WM in relation to early mathematics and language skills. Generally, children who exhibited higher WM abilities outperformed their peers who had lower WM capabilities. Conclusion: This study emphasizes the critical role of WM in early childhood education. Children with limited WM function are at a heightened risk of academic underachievement. Furthermore, both components of WM--simple WM and complex WM--emerge as influential factors in shaping children's proficiency in early literacy and numeracy skills.

The effectiveness and program mechanisms of a whole-school anti-bullying program that builds on the socio-ecological framework were investigated by applying person-oriented methods of data analyses. A longitudinal cluster randomized control study was utilized comprising 1377 adolescents (48.5% girls, M[subscript age] = 11.7) who participated in a 1-year whole school anti-bullying program that was implemented in 13 schools and 665 adolescents (45.2% girls, M[subscript age] = 11.6) who attended 5 control schools. Student reports were collected at pre- and post-test within a 1-year interval. Applying latent profile analyses (LPA), students who were actively involved in bullying as perpetrators (e.g., bullies), who were the target of bullying (e.g., victims), who were both (e.g., bully-victims), and who were not involved either as perpetrators or victims (e.g., uninvolved) were identified. Latent transition analyses (LTA) investigated transition patterns between bully, victim, bully-victim, and uninvolved subgroups in the control and intervention group before and after the 1-year program implementation. Program effectiveness (e.g., a higher transition probability in the intervention compared to the control group) were found for victims and bully-victims. It was explored whether changes in program mechanisms were differently related to changes in these sub-group memberships in the intervention group. Victims transiting to the uninvolved sub-group increased in help-seeking and anti-bullying norms, while bully-victims transiting to the uninvolved sub-group increased help-seeking, perceived teacher intervention, and anti-bullying norms. It can be concluded that this program works differently for different sub-groups of students and that different mechanisms of change drive changes in different sub-groups of students.

An individual competence is one of the main human resources, which enables a person to operate in everyday life. A competence profile, formally captured and described as a structured model, may enable various operations, e.g., a more precise evaluation and closure of a training gap. Such application scenarios supported by information systems are particularly compelling for the era of digitalisation, although research on adequate models capturing competence profiles is still lacking; moreover, no research was revealed synthesizing models of competence, enabling operationalisation possibilities. To fulfil this gap, current research develops a classification of competence models in the form of taxonomy, derived from operational characteristics of competence constructs. Given conceptual fuzziness of the competence term and complex, interdisciplinary scope of the study, the research method follows integrative literature review principles: results of an extensive search conducted in three iterations were critically analysed and further synthesized in the form of taxonomy. This critical analysis was performed based on an overview of twenty-four competence models with a lens of working definitions of competence framework and model concepts. As a result, all three outcomes highlight the power of competence models: (1) the overview summarises models' development methods, operationalisation, and purposes in a specific application domain, while (2) working definitions and (3) the taxonomy aim at overcoming a conceptual ambiguity of competence concepts. In addition, the presented taxonomy may serve as a knowledge base or a decision support tool on competence model selection when it comes to development of a competence management tool.

The purpose of this study was to address the role of social comparison in determining individual development by exploring the relationship of four types of social comparison with academic persistence-procrastination and subjective well-being among Chinese adolescents (n = 530, M[subscript age] = 14.59) from both variable- and person-centered perspectives. Results showed that the paths from social comparison to outcome were significant in most cases. Upward contrast (i.e., focusing on differences with superior targets) and downward identification (i.e., focusing on similarities with inferior targets) seemed detrimental, but upward identification (i.e., focusing on similarities with superior targets) and downward contrast (i.e., focusing on differences with inferior targets) seemed beneficial for adolescent development. Moreover, four social comparison profiles (moderate, 52.1%; high, 14.3%; upward contrast, 5.7%; and positive interpretation, 27.9%) emerged. The upward contrast profile was the least adaptive, and the positive interpretation profile (with high upward identification and downward contrast) was the most adaptive. This study provides new insights for understanding the role of social comparison in adolescent development and how to make more constructive use of it.

Research on the relationships between the main constructs underlying inquiry-based learning is rarely reported in mathematics education research. Considering this as a complex problem which is worth to be investigated, the present study aims to provide some empirical evidences that might serve as an insight to support further investigations on the relationships between attitudes towards mathematics and inquiry-based learning approaches. Thus, this study adopts a descriptive research design where no variables are manipulated but observed and measured in order to identify changes depicted in data collection. An instructional design focusing on the nature of mathematical inquiry is carried out with the participation of 304 secondary and high school students, and a clustering approach is used to look at how participants are grouped around certain attitudinal profiles before and after such mathematical practice. The results show how the heterogeneity of attitudinal profiles present in the classroom evolves positively in terms of perceived usefulness of mathematics and mathematical self-concept as perception of competence in mathematics. This fact provides some basis that might be used for further research on the idea that certain forms of development in "inquiry-based mathematics education (IBME)" based on greater immersion in the nature and culture of mathematics can help students to improve their attitudes towards mathematics.

Autistic adults experience high rates of unemployment, yet research investigating what predicts employment success produces inconsistent results. By utilising longitudinal person-oriented analyses, this study aimed to identify employment trajectories of autistic adults to better understand what may predict stable autistic employment. Participants were 2449 autistic adults (1077 men, 1352 women, 20 non-binary, M = 42.25 years, SD = 14.24), recruited via the Netherlands Autism Register. Latent class analysis utilising employment status across eight annual waves was used to identify longitudinal employment profiles. Fit indices and the interpretability of results indicated a four-class model best fit the data, with profiles reflecting stable unemployment (n = 1189), stable employment (n = 801), early unemployment increasing in probability of employment (n = 183) and high probability of employment reducing across time to low employment (n = 134). Multinominal analyses suggested that compared to the 'stable unemployment' group, membership in the 'stable employment' profile was predicted by fewer autistic traits, lower age, male gender, higher education and diagnosis age, and fewer co-occurring conditions. Higher education predicted both other profiles, with lower age and fewer co-occurring conditions predicting membership in the increasing employment class. Taken together, findings highlight the utility of person-oriented approaches in understanding the longitudinal challenges autistic adults experience maintaining employment and identifies key areas of support.

Media literacy has been gaining traction as a part of higher education curricula to support learning as educational institutions are recognizing the importance of developing students' media literacy skills. However, students' emotional reactions towards media literacy can be vastly different and, in turn, may impact their perception of truth and credibility of mixed media messages. In this study, we explored 68 non-health professions university students' unique emotional profiles towards media literacy. We further examined whether students with different emotional profiles would differ in their credibility ratings (truthfulness, trustworthiness, and believability) of media messages that were drawn from mainstream and fake news sources. We also investigated the relationship between emotion profiles and emotional reactions towards mainstream versus fake news messages. We employed a probabilistic, latent clustering approach, latent profiling analysis (LPA), to generate latent categories of emotion profiles. LPA revealed four distinct emotion profiles that students endorsed: (1) low emotions, (2) moderate emotions, (3) high negative emotions, and (4) high positive emotions towards learning media literacy. Additional findings revealed that students with a low emotional profile tended to rate all media messages as more truthful, trustworthy, and believable than other emotion groups. Moreover, we identified that students in the moderate emotions and high negative emotions group rated fake messages with more positive emotions. This study offers insight towards the significance of understanding how emotions towards media literacy can impact the outcomes of media perception. This is an important step that will encourage educators to develop more engaging media literacy instruction and interventions.

In recent years, researchers have shifted from pathologizing Black youths, when investigating the Black-White achievement gap, to focusing on factors contributing to their resilience and success. A critical review was conducted of three widely cited asset-based theoretical frameworks that are used to examine African American students' academic achievement. The review delineates the contributions and limitations of each model when studying African American students. In doing so, a new framework, linking the strengths of all three existing models, is presented as a tool to challenge existing educational practices and foster new theoretical and methodological contributions to this area of research.

Previous research has concentrated on psychological resilience as a holistic structure and has found that resilience positively affects college adjustment. However, it is suggested by existing literature that college students' resilience is a multidimensional construct. Furthermore, there is limited understanding regarding the categorizations of resilience among first-year college students and its influence on their college adjustment. This study aimed to identify the profiles of first-year college students' resilience, including individual powers and supportive powers, and explore the effects of varying resilience profiles on first-year students' college adjustment. A valid sample of 771 first-year college students from 28 Chinese colleges participated in this survey. The online surveys were conducted through Wenjuanxing platform. Data were analyzed using latent profile analysis to identify first-year college students' different resilience profile and examine the relationships with college adjustment. Four groups featuring unique resilient characteristics were discovered: well-resilient students, students with low individual power but high supportive power, students with high individual power but low supportive power, and mal-resilient students with low individual power and low supportive power. Well-resilient first-year students reported the best condition of college adjustment compared to the other groups, which supports the conservation of resources (COR) theory. These results provide insight into first-year college students' particular and heterogeneous circumstances at the beginning of the college experience. They also refined the COR theory by identifying individual-specific relationships between psychological resilience and college adjustment.

Research on learning strategies and learning motivation in different educational contexts has provided valuable insights, but in this field, low-educated adults remain an understudied population. This study addresses this gap by means of a person-oriented approach and seeks to investigate whether quantitatively and qualitatively different learner profiles can be distinguished among low-educated adults in second-chance education (SCE) by relating three key components of learning: learning motivation, regulation and processing strategies. Five hundred twelve adult learners of six SCE-institutions filled in a Learning and Motivation questionnaire. Latent profile analysis showed the presence of motivational profiles differing both in quantity and quality (i.e. good- versus poor-quality and high- versus low-quantity motivational profiles) and regulatory profiles being distinct in the use of regulation strategies (i.e. self-regulated versus unregulated profiles). Mainly quantitatively different processing profiles were found among low-educated adults (i.e. active, moderate, inactive processing profiles). When integrating all three components of learning, analyses identified two more optimal motivational-learning profiles, combining good-quality motivation with a moderately active use of self-regulation and processing strategies (i.e. good-quality motivation -- self-regulated -- active processing profile and good-quality motivation -- moderate profile) and two more suboptimal profiles in which poor-quality or low-quantity motivation was combined with the inactive use of self-regulation and processing strategies (i.e. poor-quality motivation -- unregulated -- inactive processing profile, low-quantity motivation -- unregulated -- inactive processing profile). A fifth motivational-learning profile exhibited a pattern of poor-quality motivation combined with a moderately-active use of self-regulation and processing strategies.

A review of research on the relationship between academic engagement and burnout reveals three research gaps as most of the research was conducted: i) without analysing all its multiple dimensions; ii) from a variable-centred perspective; and iii) in educational contexts other than higher education. We seek to address these gaps and thus enhance our understanding of the nature of the mentioned relationship. Adopting a person-centred perspective, a latent profile analysis (LPA) was used to identify how all the dimensions of academic engagement and burnout combine in different profiles of higher education students (n=430). Additional analyses were used to validate these LPA profiles by relating them to a set of auxiliary variables (i.e., predictors and outcomes), grounded on theoretical models relevant to higher education. LPA revealed three ordered profiles (burned-out, moderately engaged and engaged) and the additional analyses detected statistically significant associations between predictors (e.g., perceptions of academic quality, perceptions of stress) and profile membership; and between these and outcomes. The latter tended to be ordered from the least to the most desirable in learning strategies (e.g., self-regulation, deep processing) and learning outcomes (e.g., generic skills, satisfaction), with the most desirable generally being associated more with the two engaged profiles than with the burned-out profile. Taken together, the findings i) expand our understanding of the nature of academic engagement and burnout in higher education, suggesting that they are related but independent constructs at different levels (high/low and weaker levels), and ii) hold implications for theory, methodology and educational practice (adjusted to the distinctiveness of the detected profiles).

The present study investigated the profiles of motivational regulation strategies for writing (MRSW) and examined the relationship between learners' use of MRSW and their English writing performance across different profiles. L2 Writing Strategies for Motivational Regulation Questionnaire (L2WSMRQ) was administered to examine the potential profiles with a total of 604 senior high school students. Latent profile analysis yielded three subgroups of motivational regulation strategies for writing: limited, modest and competent regulators. The results of multi-group structural equation modeling revealed the significant effects of motivational regulation strategies on English writing in all three profiles. More importantly, the present study found that the relation between motivational regulation strategies and English writing performance varied across the three profiles. The most salient effect that motivational regulation strategies had was on the learners in the modest MRSW profiles, followed by the competent and limited MRSW profile, displaying a low-high-low pattern. These findings may provide pedagogical implications for EFL teachers.

School dropout is a significant issue in distance learning, and early detection is crucial for addressing the problem. Our study aims to create a binary classification model that anticipates students' activity levels based on their current achievements and engagement on a Canadian Distance learning Platform. Predicting student dropout, a common classification problem in educational data analysis, is addressed by utilizing a comprehensive dataset that includes 49 features ranging from socio-demographic to behavioral data. This dataset provides a unique opportunity to analyze student interactions and success factors in a distance learning environment. We have developed a student profiling system and implemented a predictive approach using XGBoost, selecting the most important features for the prediction process. In this work, our methodology was developed in Python, using the widely used sci-kit-learn package. Alongside XGBoost, logistic regression was also employed as part of our combination of strategies to enhance the models predictive capabilities. Our work can accurately predict student dropout, achieving an accuracy rate of approximately 82% on unseen data from the next academic year.

This study aimed to describe the profile and practices of school nurses working at a network of educational centers in Spain. This was a descriptive study of the documented actions of 107 school nurses between September 2018 and June 2021 in 54 educational centers (55.6% private and 44.4% subsidized). The profile of the school nurses was young (average age 33.8 [standard deviation (SD) = 7.7] years) and predominantly female (91.6%) with a diverse and multidisciplinary education, primarily at the postgraduate level (specialized mainly in emergency care, nursing/school health, and pediatrics/neonatology). They carried out 256,499 interventions. The most frequent types of incidents they treated were accidents (30.4%) and disease-related episodes (22.2%). The interventions were usually brief (average time 7.7 min) and were resolved by the school nurse (99.1%), and the main recipients were students (87.3%). The highest incidence of interventions occurs during breaks between classes. Acute interventions occupied most of the school nurses' time, leaving little opportunity for health education (0.3%). School nurses played an important role in preserving and promoting the health of school populations and cost-savings to healthcare systems with the actions that they performed. Descriptions of these actions are essential when advocating for the continuation and expansion of school nursing services.

In this article, student video visualization profiles are analyzed with two objectives: 1) to identify difficult sections in videos and 2) to predict student performance based on their video visualization profiles. For identifying critical sections in videos two novel indicators are proposed. The first one is designed to measure the complexity of the concept being described. The second proposal, identifies video sections that are more visually complex. For the first indicator, the average number of forward and backward passes are used. The higher the number of backward (forward) passes over a region, the more challenging (easy) the section is. For identifying sections with complex visuals, the number of pauses is recorded. Finally, the student performance prediction is carried out with the purpose of detecting the alignment between videos and their related questions. The results show that video visualization profiles are a good tool to identify video and question alignment.

This study examined the heterogeneity of literacy profiles for adolescents with and without a specific learning disability (SLD) in the U.S. state of Florida Student subgroups displaying common patterns of performance in word-level skills and reading comprehension were identified through latent profile analysis. Results indicate most of the total sample demonstrated below-average performance in one or both areas with word-level skill difficulties being more common than difficulties in reading comprehension alone. Changes in reading performance by profile over time (Grades 6-8) were examined through a latent transition analysis revealing consistent patterns in the SLD sample and variable patterns in the typically developing sample. Resulting profiles were utilized to predict performance on an end-of-year broad reading comprehension measure indicating very little change in performance over time. Findings suggest large numbers of adolescents with concurrent word-level and reading comprehension difficulties likely need sustained intervention in word-level skills to support their reading comprehension.

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace-based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self-reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self-efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self-reports collected before the start of the MOOC offers better understanding of trace-based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self-reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow.

Empirical research on number sense to date has been conducted with preschoolers and elementary school students. Despite their contributions to the literature, these studies have used variable-centered analytic approaches that may prevent distinct number sense profiles for preschoolers and first graders from being identified. This study aimed to determine the number and proportions of distinct number sense profiles, the variables influencing these profiles, and the characteristics of the children with different profiles in the transition from preschool to elementary school. We conducted a quantitative study using a cross-sectional design for this purpose and used latent profile analysis to identify and describe distinct number sense profiles for preschoolers and first-grade students. A total of 1688 children, 848 from preschool and 840 from the first grade of elementary school, took part in the study. A Number Sense Screener was used to determine number sense profiles. The data collection tool used in the study was found to be reliable. Based on the findings of the study, we identified three profiles for both the preschoolers and first graders, namely, moderate very low, moderate low, and moderate high. We examined family and household income variables to help predict the profiles. We then took the characteristics and components of number sense that caused the differentiation in the profiles as revealed by the results of the study and discussed them in light of the literature.

This study examined teachers' digital tool use through latent class analysis (LCA) and identified predictors that separated the emergent subgroups of teachers. Data from the PISA 2018 teacher questionnaire were employed to analyze the teachers' digital tool behaviors in ICT (Information and Communications Technology) strong countries: Germany, Korea, and USA. The LCA suggested three subgroups of teachers, with similar percentages in each country: 'minimal users' (Class 1: 22-33%); 'moderate users' (Class 2: 46-60%); and 'versatile users' (Class 3; 17-21%). Cross-national similarities were also found in the profile patterns of 'minimal users' and the variables predicting the subgroup membership. The single most important factor of digital tool use across the three countries was teacher training in ICT, either during pre-service training or through in-service professional development programs. Notable cross-national differences were found, however, in the profiles of 'moderate' users and their use of specific types of digital tools. Discussion concludes with practical suggestions to enhance teacher use of digital tools.

CDKL5 deficiency disorder (CDD) results in early-onset epilepsy and lifelong cognitive and motor impairments. With no validated measure for communication in CDD, this study evaluated the psychometric properties of the Communication and Symbolic Behavior Scales-Developmental Profile Infant Toddler Checklist (CSBS-DP ITC). Caregivers (n = 150; affected individuals aged 1-29 years) completed the CSBS-DP ITC. Distribution of scores indicated a floor effect. There was poor divergent validity for the three-factor model but goodness of fit and convergent validity data were satisfactory for the one-factor model. Individuals with poorer overall functional abilities scored lower on the CSBS-DP ITC. Test-retest reliability was excellent. The floor effect could explain the very high reliability, suggesting problems as a sensitive outcome measure in clinical trials for CDD.

The Kessler Psychological Distress Scale (K-10) is widely utilized to assess non-specific psychological distress. However, when applied to COVID-19 pandemic experiences, its psychometric properties have not been examined. This study investigated the factor structure, reliability, and latent profile of K-10 using data from 3032 Filipino teachers who reflected on their pandemic-related adversity prior to completing K-10. Confirmatory factor analyses were conducted to evaluate five extant models of the K-10's structure, supporting the two-factor model (i.e., anxiety and depression) as best fitting the data. Both factors showed excellent internal reliability, and criterion-related validity was supported by correlations with anxiety and depression measures. Latent profile analysis revealed a four-class solution, identifying four distinct levels of distress severity. Overall, the contextualized K-10 demonstrated sound psychometric properties, which corroborates and extends its reliability and validity as a measure of pandemic-related distress. Further, the results offer insights into the dimensionality and distress profiles of the K-10 among teachers. Broadly, the findings highlight the importance of valid instruments in assessing contextualized psychological distress.

The exploration of higher education (HE) teachers' approaches to teaching has mainly been done using quantitative instruments which have been criticised for being too narrow in examining the dimensions of teaching. "Higher education approaches to teaching" (HEAT) inventory is a recently developed instrument to capture the dimensions of HE teaching more broadly. Moreover, teacher self-efficacy has been shown to be an important element influencing approaches to teaching, but still, the research on it has been scarce. The aims of the study are thus (1) to validate the HEAT inventory and (2) to explore what kind of individual teaching profiles can be identified among higher education teachers and their relation to teachers' self-efficacy. The results showed that HEAT is a valid instrument to measure various dimensions of approaches to teaching and that several profiles of approaches to teaching can emerge among HE teachers. These profiles also differ in terms of teachers' self-efficacy.

A detailed, unbiased perspective of the inter-relations among medical fields could help students make informed decisions on their future career plans. Using a data-driven approach, the inter-relations among different medical fields were decomposed and clustered based on the similarity of their working environments. Publicly available, aggregate databases were merged into a single rich dataset containing demographic, working environment and remuneration information for physicians across Canada. These data were collected from the Canadian Institute for Health Information, the Canadian Medical Association, and the Institute for Clinical Evaluative Sciences, primarily from 2018 to 2019. The merged dataset includes 25 unique medical specialties, each with 36 indicator variables. Latent Profile Analysis (LPA) was used to group specialties into distinct clusters based on relatedness. The 25 medical specialties were decomposed into seven clusters (latent variables) that were chosen based on the Bayesian Information Criterion. The Kruskal-Wallis test identified eight indicator variables that significantly differed between the seven profiles. These variables included income, work settings and payment styles. Variables that did not significantly vary between profiles included demographics, professional satisfaction, and work-life balance satisfaction. The 25 analyzed medical specialties were grouped in an unsupervised manner into seven profiles via LPA. These profiles correspond to expected and meaningful groups of specialties that share a common theme and set of indicator variables (e.g. procedurally-focused, clinic-based practice). These profiles can help aspiring physicians narrow down and guide specialty choice.

We aimed to identify unique constellations of sensory phenotypes for genetic etiologies associated with diagnoses of autism spectrum disorder (ASD) and intellectual disability (ID). Caregivers reported on sensory behaviors via the Sensory Profile for 290 participants (younger than 25 years of age) with ASD and/or ID diagnoses, of which [approximately] 70% have a known pathogenic genetic etiology. Caregivers endorsed poor registration (i.e., high sensory threshold, passive behaviors) for all genetic subgroups relative to an "idiopathic" comparison group with an ASD diagnosis and without a known genetic etiology. Genetic profiles indicated prominent sensory seeking in "ADNP," "CHD8," and "DYRK1A," prominent sensory sensitivities in "SCN2A," and fewer sensation avoidance behaviors in "GRIN2B" (relative to the idiopathic ASD comparison group).

This study investigated the extent to which sensory responsivity in infancy contributes to adaptive behavior development among toddlers at high-familial likelihood for autism. Prospective, longitudinal data were analyzed for 218 children, 58 of whom received an autism diagnosis. Results indicated that sensory profiles at age one year (hyperresponsivity, sensory seeking) were negatively associated with later adaptive behavior, particularly for socialization, at age 3 years regardless of diagnostic status. These results suggest that early differences in sensory responsivity may have downstream developmental consequences related to social development among young children with high-familial likelihood for autism.

International students are at risk of burnout during their studies and face challenges in their writing. This study explores the burnout and self-efficacy profiles of international students and how these profiles differ in writing conceptions. Participants were 162 international students at a research-intensive Finnish university. They completed the "HowULearn Questionnaire and the Writing Process Questionnaire". Latent profile analysis identified three profiles combining students' burnout and self-efficacy: "high burned-out students" (16.0%), "moderate burned-out students" (45.1%), and "low burned-out students with high self-efficacy" (38.9%). The profile membership was not significantly related to students' demographic, linguistic, or writing-related characteristics. The results showed that the combination of a lower level of study-related burnout and relatively higher self-efficacy led to fewer blocks, less procrastination and perfectionism, and more positive conceptions of knowledge transforming and productivity in writing. Overall, the study indicated that study-related burnout and self-efficacy contributes to understanding international students' writing processes.

This study explores the effectiveness of online and blended teaching methods in developing cross-curricular skills crucial for an ideal engineering profile among undergraduate students. Involving 587 online and 635 blended students across 11 Latin American universities, the research incorporates Likert tests for student perceptions and Mann-Whitney statistical tests for hypothesis scrutiny. Challenges emerged due to diverse academic contexts and the subjective nature of self-reported perceptions. Despite these hurdles, findings highlight the blended method's superior effectiveness in fostering key engineering skills. The study not only contributes to teaching method exploration but also addresses challenges in cross-cultural research settings, enhancing our understanding of the complexities and adaptations required for cross-curricular skill development in engineering education.