Search Results (99)

Acquiring prosodic focus marking in a second language (L2) is difficult for learners whose native language utilizes strategies that differ from those of the target language. German typically uses pitch accents (L+H*/H*) to mark focus, while (Modern Standard) Arabic preferably employs a syntactic strategy (word order) or lexical means. In Syrian Arabic, a variety which is predominantly oral, pitch accents are used to mark focus, but the distribution and types are different from German. The present study investigates how Syrian Arabic learners of German prosodically mark focus in L2 German. A question–answer paradigm was used to elicit German subject-verb-object (SVO)-sentences with broad, narrow, or contrastive focus. Productions of advanced (C1, N = 17) and intermediate (B1/B2, N = 8) Syrian Arabic learners were compared to those of German controls (N = 12). Like the controls, both learner groups successfully placed pitch accents on focused constituents. However, learners, especially those with lower proficiency, used more pitch accents in non-focal regions than the controls, revealing challenges in de-accentuation. These may result from the larger number of phrase boundaries in learners’ productions, which in turn might be explained by transfer from the L1 or aspects of general fluency. Learners also differed from the controls with respect to accent type. They predominantly used H* for narrow or contrastive focus (instead of L+H*); proficiency effects played only a minor role here. Our study hence reveals an intricate interplay between cross-linguistic influence and proficiency in the L2 acquisition of prosodic focus marking, targeting a language pair so far underrepresented in the literature (German vs. Syrian Arabic). Full article

The tonal system of Cantonese is very different from that of Mandarin, which creates potential challenges for Mandarin speakers when learning Cantonese. The aim of this study was to explore second language (L2) production of Cantonese tones by advanced learners whose first language (L1) is Mandarin. Forty-one informants participated in a recording experiment to provide production data of Cantonese tones. The speech data were measured acoustically using the computer software Praat (Version 6.3.10) and were evaluated perceptually by native Cantonese speakers. The relationship between the acoustic analysis and perceptual evaluation was also explored. The acoustic and perceptual evaluations confirmed that, while the tones that the Mandarin learners of Cantonese produced were non-native-like, their production of the Cantonese T1 and T2 was good in general. Furthermore, the accuracy of the perceptual evaluations could be predicted based on the acoustic features of the L2 tones. Our findings are in line with hypotheses in current speech learning models, and demonstrate that familiar phonetic categories are easier to acquire than are unfamiliar ones. To provide a more complete picture of L2 speech acquisition, future research should investigate L2 tone acquisition using both production and perception data obtained from participants with a greater variety of L1s. Full article

A wide variety of requirements can be placed on tasks that deal with education for sustainable development in mathematics lessons. They should be as authentic as possible, use real problems as the mathematical learning material, and stimulate action, among other qualities. This article discusses the role of self-efficacy and the experience of self-efficacy when working on modelling tasks that are geared towards a sustainable future. High school students in Germany worked in a STEM learning environment on different aspects of climate change and species extinction, including plastic waste, recycling, rainforests, and their deforestation. These aspects were analysed from a geographical, biological, physical, and mathematical perspective. In mathematics, specifically, tasks were used to address the learners’ self-efficacy. After completing the tasks, a questionnaire was distributed to assess the interest and motivation of the learners. The results show that even a slightly different use of self-efficacy, whether by focusing on what has already been achieved (sustainable successes that promote positive emotions) or on what can still be achieved, can influence the learners’ interest in completing the tasks. The learners’ experience of self-efficacy seems to have a positive influence on their willingness to solve tasks. Additionally, the results indicate a complex relationship between motivation and interest on the one hand and self-efficacy on the other. Full article

Previous studies have found that second language learners can acquire sociolinguistic variation. However, there is a lack of studies that examine the L2 acquisition of second-person singular forms of address (2PS) in Spanish, especially in the immersion context of study abroad. The current study examines the acquisition of Spanish 2PS by seven adults learning Spanish in Medellin, Colombia. Participants completed an oral discourse completion task and a matched guise task to measure language perceptions toward each 2PS. Learners’ results are compared to findings from 38 native Spanish speakers from Medellin. Learners produced very few instances of the local variant vos and overproduced tú, differing greatly from native speakers. Two factors were found to significantly condition 2PS usage for learners: speaker gender and interlocutor relationship. Findings show that although learners perceive vos to a somewhat native-like extent and the role that it plays in the local variety, learners do not actually use it. Full article

In today’s globalized world, foreign language (FL) communication is characterized by the presence of regional variations that can impact L2 learners’ speech perception in their target language. While it is essential for FL programs to prepare their students for real-world language variation, research on learner perception of spoken regional varieties remains scarce, especially for less commonly taught languages, such as Italian. To address this, this study used a quantitative approach to explore to what extent listeners’ background factors (i.e., accent familiarity, contact with Italian speakers, heritage learner status, L1) and speaker accent impact FL learners’ perceptions of (1) intelligibility, (2) comprehensibility, and (3) acceptability of regionally accented Italian speech. A total of forty-seven FL learners of Italian listened to the speech of six Italian native speakers with different regional accents (two each from Northern, Central, and Southern Italy), rated them for comprehensibility and acceptability, and transcribed utterances for intelligibility. Mixed-effects models revealed statistically significant effects of speaker accent and listener L1 background on all three perceptual constructs. Additionally, greater contact corresponded with higher comprehensibility, while heritage learners rated speech as less acceptable for educational contexts. Listeners’ overall positive perception of regionally accented speech encourages the introduction of authentic spoken varieties in the FL classroom. Full article

In recent years, several studies across a variety of target languages (e.g., Chinese, French, and Spanish) have demonstrated that students who study abroad acquire target-like patterns of variation. In Spanish-speaking contexts, recent research has moved beyond investigating the acquisition of features specific to Spain to examine that of features used in immersion contexts such as Mexico, the Dominican Republic, Ecuador, Peru, and Argentina. Nevertheless, many of these studies either rely on quantitative variationist analysis or implement qualitative analysis of one or two target dialectal features. In addition, learner omission and expression of pronominal subjects in these contexts have been largely underexplored. Using a mixed-methods approach, this study not only quantitatively examines learners’ production of several features of Buenos Aires Spanish, including sheísmo/zheísmo, /s/-weakening, voseo, and subject pronoun expression, but it also qualitatively relates the production of these features to learners’ experiences during a five-month semester in Argentina. It aims to answer the following research questions: When and to what degree do three English-speaking students studying abroad for five months in Buenos Aires, Argentina acquire target-like production of [ʃ] and/or [ʒ], s-weakening, vos, and subject pronoun expression? How do participants’ experiences, communities of practice, investments, identities, and imagined communities relate to this production? Speech data were gathered prior to, at the midpoint, and at the end of the semester by means of sociolinguistic interviews and elicitation tasks. To further understand the connection between these learners’ use of the target features and their overseas experiences, we explored the case studies of three learners of Spanish of differing proficiency levels (beginning, intermediate, and advanced) using qualitative data collected during semi-structured interviews at each interview time. The results suggest that all three learners increased their production of the prestigious, salient dialectal features of sheísmo/zheísmo and vos during the sojourn and that the amount of increase was greater at each proficiency level. While the beginning and intermediate learners did not move toward target-like norms in their use of the often-stigmatized, less salient, variable features of /s/-weakening and subject pronoun expression, the advanced learner did. As such, stigma, salience, and variability, as well as proficiency level, may play a role in the acquisition of variable features. Learners’ investment in the target language and participation in local communities of practice increased at each proficiency level as well, and learners’ imagined communities beyond their study abroad experiences were related to their identity construction and linguistic choices abroad. Full article

This paper describes large, fine-grained, intentionally inclusive, research-based cognitive models called learning maps. Learning maps are the product of an intensive research synthesis process to develop formal learning models that better reflect the diversity of how learners can learn and be assessed on academic content. Students begin at different places and learn at different rates, and they may have cognitive disabilities or may face a variety of barriers that pose challenges when learning content. Learning maps provide numerous starting points and pathways by which students can acquire and demonstrate knowledge, skills, and understandings. Our work in developing learning maps relies on principles of Universal Design for Learning (UDL), which provides a foundation of flexibility and inclusivity to accommodate students with a wide range of cognitive, linguistic, physical, and sensory profiles. In this paper, we describe learning map design, development, and both qualitative and quantitative methods for the evaluation of map structure. In addition, we offer reflections on our experiences with implementing learning maps as the cognitive architecture for assessments and educational interventions through our work on a variety of projects. With examples from these projects, we describe evidence that shows how learning maps can be useful tools for improving instruction and assessment for all learners. We identify areas where further research and inquiry could prove fruitful and conclude with a discussion of potential areas of extension and offer suggestions for the ongoing refinement of learning maps. Full article

This scoping review provides clarity on the landscape of evidence-based and High-Leverage Practices that have been shown to be effective with students with disabilities and have the potential to meet the needs of marginalized students across lines of difference. Of 672 articles screened, 85 met eligibility criteria, including 46 studies, 11 systematic reviews, and 28 conceptual papers. Among included articles, instruction practices were the most frequently reported High-Leverage Practice category (89.4%), followed by social, emotional, and behavioral practices (37.6%), and assessment practices (25.8%). A wide variety of specific evidence-based practices were identified in the literature. Marginalized student identities represented included English language learners, students with disabilities, neurodivergent students, racially or ethnically marginalized students, students with health disabilities, and students with behavioral or emotional difficulties. Future research should consider further examining the effectiveness of different practices to inform data-driven decision-making to improve educational outcomes for marginalized students. Full article

Tablets have long been employed across a variety of educational domains. Given the ongoing debate regarding their effect on skill and knowledge development, especially among very young learners, a study was conducted to evaluate their effectiveness against conventional educational materials. The study involved 76 kindergarten students, employing a between-subjects design to investigate basic literacy skills. These skills included letter name identification and letter sound recognition. To measure knowledge acquisition, evaluation tests were administered, and questionnaires were used to collect data on students’ motivation, engagement, and enjoyment. The findings revealed that tablets significantly enhanced the targeted skills compared to conventional materials. Tablets were also rated higher in enjoyment, engagement, and motivation. Importantly, these factors significantly impacted learning outcomes with tablets, while they had no effect in the case of conventional materials. Full article

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding AI-powered interventions to refine instructional strategies dynamically. Methods: This study reviews n = 103 papers related to the integration of principles of CLT with AI and ML in educational settings. It evaluates the progress made in neuroadaptive learning technologies, especially the real-time management of cognitive load, personalized feedback systems, and the multimodal applications of AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, and scalability issues while pinpointing best practices for robust and effective implementation. Results: The results show that AI and ML significantly improve Learning Efficacy due to managing cognitive load automatically, providing personalized instruction, and adapting learning pathways dynamically based on real-time neurophysiological data. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) improve classification accuracy, making AI-powered adaptive learning systems more efficient and scalable. Multimodal approaches enhance system robustness by mitigating signal variability and noise-related limitations by combining EEG with fMRI, Electrocardiography (ECG), and Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including ethical considerations, data security risks, and accessibility disparities across learner demographics. Conclusions: AI and ML are epitomes of redefinition potentials that solid ethical frameworks, inclusive design, and scalable methodologies must inform. Future studies will be necessary for refining pre-processing techniques, expanding the variety of datasets, and advancing multimodal neuroadaptive learning for developing high-accuracy, affordable, and ethically responsible AI-driven educational systems. The future of AI-enhanced education should be inclusive, equitable, and effective across various learning populations that would surmount technological limitations and ethical dilemmas. Full article

Practice and Research Optimization Environment in Python (PyPROE) is a GUI-based, integrated framework designed to improve the user experience in both learning and research on engineering design optimization. Traditional optimization programs require either coding or creating complex input files, and often involve a variety of applications in sequence to arrive at the solution, which presents a steep learning curve. PyPROE addresses these challenges by providing an intuitive, user-friendly Graphical User Interface (GUI) that integrates key steps in design optimization into a seamless workflow through a single application. This integration reduces the potential for user error, lowers the barriers to entry for learners, and allows students and researchers to focus on core concepts rather than software intricacies. PyPROE’s human-centered design simplifies the learning experience and enhances productivity by automating data transfers between function modules. This automation allows users to dedicate more time to solving engineering problems rather than dealing with disjointed tools. Benchmarking and user surveys demonstrate that PyPROE offers significant usability improvements, making complex engineering optimization accessible to a broader audience. Full article

Educational data mining (EDM) is a novel scientific area that focuses on developing and applying methods to analyze datasets generated within educational settings. This paper outlines the evolution, significance, and applications of EDM. With the increasing popularity of e-learning in web-based educational systems, EDM has expanded to include a variety of analytical methods and data sources. Some key methodologies addressed include classification, regression analysis, clustering techniques, association rule mining, and Natural Language Processing, among others. Additionally, this paper looks at how EDM can facilitate data-driven decision-making among other areas such as curriculum development and customization of learners’ experiences. It also touches on issues related to the challenges of the scientific field. Finally, some projections about EDM’s future trends are made, especially concerning its integration into AI technologies and development trends like augmented reality or virtual reality, which imply greater possibilities for changes than any other series witnessed before within this sphere. Full article

School tasks often include individual and collaborative activities supported by a wide variety of learning materials. These materials can elicit varied levels of attention and learning depending on the complexity (i.e., element interactivity level) and physical separation of the information elements in the study material. The aim of this study was to explore the potential effects of the element interactivity level (i.e., high vs. low) and split attention (i.e., integrated vs. separated information) on individual and collaborative learning. An experimental design was implemented with 192 high school learners, with 64 working individually and 128 in dyads. The results revealed that in tasks with high element interactivity and integrated information, individual students learned more than groups. However, separated information benefited groups more than individual learners. It is concluded that the benefits of individual and group learning are mediated by task element interactivity and the physical separation of information sources in the study material, and recommendations for education professionals are presented. Full article

Simplicity in information found online is in demand from diverse user groups seeking better text comprehension and consumption of information in an easy and timely manner. Readability assessment, particularly at the sentence level, plays a vital role in aiding specific demographics, such as language learners. In this paper, we research model evaluation metrics, strategies for model creation, and the predictive capacity of features and feature sets in assessing readability based on sentence complexity. Our primary objective is to classify sentences as either simple or complex, shifting the focus from entire paragraphs or texts to individual sentences. We approach this challenge as both a classification and clustering task. Additionally, we emphasize our tests on shallow features that, despite their simplistic nature and ease of use, seem to yield decent results. Leveraging the TextStat Python library and the WEKA toolkit, we employ a wide variety of shallow features and classifiers. By comparing the outcomes across different models, algorithms, and feature sets, we aim to offer valuable insights into optimizing the setup. We draw our data from sentences sourced from Wikipedia’s corpus, a widely accessed online encyclopedia catering to a broad audience. We strive to take a deeper look at what leads to greater readability classification in datasets that appeal to audiences such as Wikipedia’s, assisting in the development of improved models and new features for future applications with low feature extraction/processing times. Full article

Elaboration is a generative learning strategy wherein learners link prior knowledge and experiences with to-be-remembered information. It is positively related to an array of learning outcomes. However, most students do not independently use generative learning strategies. We explored whether prompting elaboration learning strategies when reading an academic passage influenced knowledge test performance. Participants were randomly assigned to two conditions: receiving a prompt (i.e., experimental; n = 94) and no prompt (i.e., control; n = 112). The results revealed that participants who received the elaboration prompt (M = 13.88, SD = 2.20) did not outperform learners who did not receive the prompt (M = 13.67, SD = 2.43) on the knowledge test. However, we did find a positive relationship between the extent of elaboration strategy use and knowledge test performance across conditions (r = 0.17, p < 0.05). Twelve themes emerged from an exploratory thematic analysis, wherein participants were asked about the learning strategies they used when reading the passage. Students used a variety of learning strategies unprompted, although 42.15% reported not using any additional learning strategies outside of the prompt or using low-utility learning strategies (e.g., relying on memory, skimming). Further exploratory analyses found that the quantity and diversity of learning strategies used individually influenced knowledge test performance. ANCOVA results revealed, however, that when controlling for quantity, the diversity of learning strategies used did not significantly influence knowledge test performance. Our findings contribute to prior literature by (1) demonstrating a relationship between elaboration strategy use and test performance, (2) highlighting learning strategies students use to retain information, and (3) exploring additional factors regarding learning strategy use that influence performance. Full article