Search results for: hatton v sutherland 16 practical propositions

Authors: Sumeyra Karatas, Veysel Karatas, Reyhan Safak, Gamze Bulut-Ozturk, Ozgul Kartal

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Productive struggle entails a student's cognitive exertion to comprehend mathematical concepts and uncover solutions not immediately apparent. The significance of productive struggle in learning mathematics is accentuated by influential educational theorists, emphasizing its necessity for learning mathematics with understanding. Consequently, supporting productive struggle in learning mathematics is recognized as a high-leverage and effective mathematics teaching practice. In this study, the investigation into the role of productive struggle in learning mathematics led to the development of a comprehensive rubric for productive struggle pedagogy through an exhaustive literature review. The rubric consists of eight primary criteria and 37 sub-criteria, providing a detailed description of teacher actions and pedagogical choices that foster students' productive struggles. These criteria encompass various pedagogical aspects, including task design, tool implementation, allowing time for struggle, posing questions, scaffolding, handling mistakes, acknowledging efforts, and facilitating discussion/feedback. Utilizing this rubric, a team of researchers and teachers designed eight 90-minute lesson plans, employing a productive struggle pedagogy, for a two-week unit on solving systems of linear equations. Simultaneously, another set of eight lesson plans on the same topic, featuring identical content and problems but employing a traditional lecture-and-practice model, was designed by the same team. The objective was to assess the impact of supporting productive struggle on students' mathematics learning, defined by the strands of mathematical proficiency. This quasi-experimental study compares the control group, which received traditional lecture- and practice instruction, with the treatment group, which experienced a productive struggle in pedagogy. Sixty-six 10th and 11th-grade students from two algebra classes, taught by the same teacher at a high school, underwent either the productive struggle pedagogy or lecture-and-practice approach over two-week eight 90-minute class sessions. To measure students' learning, an assessment was created and validated by a team of researchers and teachers. It comprised seven open-response problems assessing the strands of mathematical proficiency: procedural and conceptual understanding, strategic competence, and adaptive reasoning on the topic. The test was administered at the beginning and end of the two weeks as pre-and post-test. Students' solutions underwent scoring using an established rubric, subjected to expert validation and an inter-rater reliability process involving multiple criteria for each problem based on their steps and procedures. An analysis of covariance (ANCOVA) was conducted to examine the differences between the control group, which received traditional pedagogy, and the treatment group, exposed to the productive struggle pedagogy, on the post-test scores while controlling for the pre-test. The results indicated a significant effect of treatment on post-test scores for procedural understanding (F(2, 63) = 10.47, p < .001), strategic competence (F(2, 63) = 9.92, p < .001), adaptive reasoning (F(2, 63) = 10.69, p < .001), and conceptual understanding (F(2, 63) = 10.06, p < .001), controlling for pre-test scores. This demonstrates the positive impact of supporting productive struggle in learning mathematics. In conclusion, the results revealed the significance of the role of productive struggle in learning mathematics. The study further explored the practical application of productive struggle through the development of a comprehensive rubric describing the pedagogy of supporting productive struggle.

Keywords: effective mathematics teaching practice, high school algebra, learning mathematics, productive struggle

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Authors: Eunjung Lee

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This research investigates the intricate connection between cultural differences and online consumer behaviors by integrating Hofstede's Cultural Dimensions theory with analysis methodologies such as text mining, data mining, and topic analysis. Our aim is to provide a comprehensive understanding of how national cultural differences influence individuals' behaviors when engaging with online reviews. To ensure the relevance of our investigation, we systematically analyze and interpret the cultural nuances influencing online consumer behaviors, especially in the context of online reviews. By anchoring our research in Hofstede's Cultural Dimensions theory, we seek to offer valuable insights for marketers to tailor their strategies based on the cultural preferences of diverse global consumer bases. In our methodology, we employ advanced text mining techniques to extract insights from a diverse range of online reviews gathered globally for a specific product or service like Netflix. This approach allows us to reveal hidden cultural cues in the language used by consumers from various backgrounds. Complementing text mining, data mining techniques are applied to extract meaningful patterns from online review datasets collected from different countries, aiming to unveil underlying structures and gain a deeper understanding of the impact of cultural differences on online consumer behaviors. The study also integrates topic analysis to identify recurring subjects, sentiments, and opinions within online reviews. Marketers can leverage these insights to inform the development of culturally sensitive strategies, enhance target audience segmentation, and refine messaging approaches aligned with cultural preferences. Anchored in Hofstede's Cultural Dimensions theory, our research employs sophisticated methodologies to delve into the intricate relationship between cultural differences and online consumer behaviors. Applied to specific cultural dimensions, such as individualism vs. collectivism, masculinity vs. femininity, uncertainty avoidance, and long-term vs. short-term orientation, the study uncovers nuanced insights. For example, in exploring individualism vs. collectivism, we examine how reviewers from individualistic cultures prioritize personal experiences while those from collectivistic cultures emphasize communal opinions. Similarly, within masculinity vs. femininity, we investigate whether distinct topics align with cultural notions, such as robust features in masculine cultures and user-friendliness in feminine cultures. Examining information-seeking behaviors under uncertainty avoidance reveals how cultures differ in seeking detailed information or providing succinct reviews based on their comfort with ambiguity. Additionally, in assessing long-term vs. short-term orientation, the research explores how cultural focus on enduring benefits or immediate gratification influences reviews. These concrete examples contribute to the theoretical enhancement of Hofstede's Cultural Dimensions theory, providing a detailed understanding of cultural impacts on online consumer behaviors. As online reviews become increasingly crucial in decision-making, this research not only contributes to the academic understanding of cultural influences but also proposes practical recommendations for enhancing online review systems. Marketers can leverage these findings to design targeted and culturally relevant strategies, ultimately enhancing their global marketing effectiveness and optimizing online review systems for maximum impact.

Keywords: comparative analysis, cultural dimensions, marketing intelligence, national culture, online consumer behavior, text mining

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Authors: Lina Wu

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The excessive discharge of nitrogen in sewage greatly intensifies the eutrophication of water bodies and threatens water quality. Nitrogen pollution control has become a global concern. The concentration of nitrogen in water is reduced by converting ammonia nitrogen, nitrate nitrogen and nitrite nitrogen into nitrogen-containing gas through biological treatment, physicochemical treatment and oxidation technology. However, some wastewater containing high ammonia nitrogen including landfill leachate, is difficult to be treated by traditional nitrification and denitrification because of its high COD content. The core process of denitrification is that denitrifying bacteria convert nitrous acid produced by nitrification into nitrite under anaerobic conditions. Still, its low-carbon nitrogen does not meet the conditions for denitrification. Many studies have shown that the natural autotrophic anammox bacteria can combine nitrous and ammonia nitrogen without a carbon source through functional genes to achieve total nitrogen removal, which is very suitable for removing nitrogen from leachate. In addition, the process also saves a lot of aeration energy consumption than the traditional nitrogen removal process. Therefore, anammox plays an important role in nitrogen conversion and energy saving. The short-range nitrification and denitrification coupled with anaerobic ammoX ensures total nitrogen removal. It improves the removal efficiency, meeting the needs of society for an ecologically friendly and cost-effective nutrient removal treatment technology. In recent years, research has found that the symbiotic system has more water treatment advantages because this process not only helps to improve the efficiency of wastewater treatment but also allows carbon dioxide reduction and resource recovery. Microalgae use carbon dioxide dissolved in water or released through bacterial respiration to produce oxygen for bacteria through photosynthesis under light, and bacteria, in turn, provide metabolites and inorganic carbon sources for the growth of microalgae, which may lead the algal bacteria symbiotic system save most or all of the aeration energy consumption. It has become a trend to make microalgae and light-avoiding anammox bacteria play synergistic roles by adjusting the light-to-dark ratio. Microalgae in the outer layer of light particles block most of the light and provide cofactors and amino acids to promote nitrogen removal. In particular, myxoccota MYX1 can degrade extracellular proteins produced by microalgae, providing amino acids for the entire bacterial community, which helps anammox bacteria save metabolic energy and adapt to light. As a result, initiating and maintaining the process of combining dominant algae and anaerobic denitrifying bacterial communities has great potential in treating landfill leachate. Chlorella has a brilliant removal effect and can withstand extreme environments in terms of high ammonia nitrogen, high salt and low temperature. It is urgent to study whether the algal mud mixture rich in denitrifying bacteria and chlorella can greatly improve the efficiency of landfill leachate treatment under an anaerobic environment where photosynthesis is stopped. The optimal dilution concentration of simulated landfill leachate can be found by determining the treatment effect of the same batch of bacteria and algae mixtures under different initial ammonia nitrogen concentrations and making a comparison. High-throughput sequencing technology was used to analyze the changes in microbial diversity, related functional genera and functional genes under optimal conditions, providing a theoretical and practical basis for the engineering application of novel bacteria-algae symbiosis system in biogas slurry treatment and resource utilization.

Keywords: nutrient removal and recovery, leachate, anammox, Partial nitrification, Algae-bacteria interaction

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Authors: Mazlum Çelik, Burcu Gürkan Ercan, Ahmet Ayaz, Hilal Yakut İpekoğlu, Furkan Baltacı, Mustafa Kurtoğlu, Bilge Kalkavan, Sinem Küçükyılmaz, Hikmet Çağrı Yardımcı, Şeyma Sevgican, Cemile Gökçe Elkovan, Bilal Çayır, Mehmet Emin Düzcan

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The earthquake research literature generally examines emotional, cognitive, and behavioral responses after an earthquake. Studies concerning the behavioral responses to earthquakes reveal that after the earthquake, people either flee in a panic or do not act according to the stereotype that they act irrationally and anti-socially and sometimes give rational and adaptive reactions. However, the rareness of research dealing with human behavior experiencing the earthquake moment makes it necessary to pay particular attention to these behavior patterns. In this direction, this study aims to examine human behavior indoors in case of rising earthquake intensity. In Turkey, located on geography in the earthquake zone, devastating earthquakes took place, such as in "Istanbul" with a magnitude of 7.4 in 1999 and in "Elazığ" with a magnitude of 6.8 in 2020. Occurred recently, the "Kahramanmaraş" earthquake affected 11 provinces, with a magnitude of 7.7 and 7.6 in 2023. In addition, there is expected to be a devastating earthquake in Istanbul, experts warn. For this reason, it is essential to understand human behavior for disaster risk. Management and pre-disaster preparedness to be effective and efficient and to take realistic measures to protect human life. Mazlum Çelik, Burcu Gürkan Ercan, Ahmet Ayaz, Hilal Yakut İpekoğlu, Furkan Baltacı, Mustafa Kurtoğlu, Bilge Kalkavan, Sinem Küçükyılmaz, Hikmet Çağrı Yardımcı, Şeyma Sevgican, Cemile Gökçe Elkovan, Bilal Çayır, Mehmet Emin Düzcan. In this study, which is currently part of a project supported by The Scientific and Technological Council of Turkey (TUBITAK), the indoor recordings during the earthquakes in Elazig on January 24, 2020, and in İzmir on October 30, 2020, are examined, and the people's behavior during the earthquake is analyzed. In this direction, video recordings taken from the YouTube archives of İzmir and Elazığ Disaster and Emergency Management Presidency (AFAD) Directorates and metropolitan municipalities are examined. The researchers have created an observation form in line with the information in the relevant literature to classify people's behavior during an earthquake. It is intended to determine the behavioral patterns by classifying according to the form and video analysis of the people heading toward the door, remaining stable, taking protective measures, turning to people, and engaging in "other" behaviors outside of these behaviors during the earthquake. A total of 60 video analyzes are carried out from Elazığ and İzmir. The descriptive statistic has been used with the SPSS 23.0 package program in the data analysis. It is found that in the event of an increase in the severity of the earthquake, unlike Elazığ, in İzmir, protective action is preferred to the act of remaining stable. In addition, it is observed that with the increase in the earthquake's intensity, women attempt to take more protective action while men head toward the door. In contrast, a rise is observed in the behavior of young people heading toward the door and taking protective actions, while there is a decrease in their behavior directing to people. These findings, unlike the literature, reveal that human behavior during earthquakes cannot be reduced to a single behavior pattern, such as drop-cover-hold-on. The results show that it is necessary to understand the behaviors of individuals during the earthquake and to develop practical policy proposals for combating earthquakes by considering sociocultural, geographical, and demographic variables.

Keywords: descriptive analysis, earthquake, human behaviour, disaster policy.

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Authors: Delia Gabriela Moisa, Demos Parapanos, Tim Heap

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The tourism sector is mainly comprised of small and medium-sized tourism enterprises (SMEs), representing approximately 80% of global businesses in this field. These entities require focused attention and support to address challenges, ensuring their competitiveness and relevance in a dynamic industry characterized by continuously changing customer preferences. To address these challenges, it becomes imperative to consider not only socio-demographic factors but also delve into the intricate interplay of psychological elements influencing consumer behavior. This study investigates the impact of personality traits and travel motivations on visitor activities in Cumbria, United Kingdom, an iconic region marked by UNESCO World Heritage Sites, including The Lake District National Park and Hadrian's Wall. With a £4.1 billion tourism industry primarily driven by SMEs, Cumbria serves as an ideal setting for examining the relationship between tourist psychology and activities. Employing the Big Five personality model and the Travel Career Pattern motivation theory, this study aims to explain the relationship between psychological factors and tourist activities. The study further explores SME perspectives on personality-based market segmentation, providing strategic insights into addressing evolving tourist preferences.This pioneering mixed-methods study integrates quantitative data from 330 visitor surveys, subsequently complemented by qualitative insights from tourism SME representatives. The findings unveil that socio-demographic factors do not exhibit statistically significant variations in the activities pursued by visitors in Cumbria. However, significant correlations emerge between personality traits and motivations with preferred visitor activities. Open-minded tourists gravitate towards events and cultural activities, while Conscientious individuals favor cultural pursuits. Extraverted tourists lean towards adventurous, recreational, and wellness activities, while Agreeable personalities opt for lake cruises. Interestingly, a contrasting trend emerges as Extraversion increases, leading to a decrease in interest in cultural activities. Similarly, heightened Agreeableness corresponds to a decrease in interest in adventurous activities. Furthermore, travel motivations, including nostalgia and building relationships, drive event participation, while self-improvement and novelty-seeking lead to adventurous activities. Additionally, qualitative insights from tourism SME representatives underscore the value of targeted messaging aligned with visitor personalities for enhancing loyalty and experiences. This study contributes significantly to scholarship through its novel framework, integrating tourist psychology with activities and industry perspectives. The proposed conceptual model holds substantial practical implications for SMEs to formulate personalized offerings, optimize marketing, and strategically allocate resources tailored to tourist personalities. While the focus is on Cumbria, the methodology's universal applicability offers valuable insights for destinations globally seeking a competitive advantage. Future research addressing scale reliability and geographic specificity limitations can further advance knowledge on this critical relationship between visitor psychology, individual preferences, and industry imperatives. Moreover, by extending the investigation to other districts, future studies could draw comparisons and contrasts in the results, providing a more nuanced understanding of the factors influencing visitor psychology and preferences.

Keywords: personality trait, SME, tourist behaviour, tourist motivation, visitor activity

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Authors: Chau So Ling

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I.INTRODUCTION: Educators have always been interested in exploring factors that contribute to students’ academic success. It is beyond question that language, as a medium of instruction, will affect student learning. This paper tries to investigate whether language is a crucial factor in determining students’ achievement in their studies. II. BACKGROUND AND SIGNIFICANCE OF STUDY: The issue of using English as a medium of instruction in Hong Kong is a special topic because Hong Kong is a post-colonial and international city which a British colony. In such a specific language environment, researchers in the education field have always been interested in investigating students’ language proficiency and its relation to academic achievement and other related educational indicators such as motivation to learn, self-esteem, learning effectiveness, self-efficacy, etc. Along this line of thought, this study specifically focused on business education. III. METHODOLOGY: The methodology in this study involved two sequential stages, namely, a focus group interview and a data analysis. The whole study was directed towards both qualitative and quantitative aspects. The subjects of the study were divided into two groups. For the first group participating in the interview, a total of ten high school students were invited. They studied Business Studies, and their English standard was varied. The theme of the discussion was “Does English affect your learning and examination results of Business Studies?” The students were facilitated to discuss the extent to which English standard affected their learning of Business subjects and requested to rate the correlation between English and performance of Business Studies on a five-point scale. The second stage of the study involved another group of students. They were high school graduates who had taken the public examination for entering universities. A database containing their public examination results for different subjects has been obtained for the purpose of statistical analysis. Hypotheses were tested and evidence was obtained from the focus group interview to triangulate the findings. V. MAJOR FINDINGS AND CONCLUSION: By sharing of personal experience, the discussion of focus group interviews indicated that higher English standards could help the students achieve better learning and examination performance. In order to end the interview, the students were asked to indicate the correlation between English proficiency and performance of Business Studies on a five-point scale. With point one meant least correlated, ninety percent of the students gave point four for the correlation. The preliminary results illustrated that English plays an important role in students’ learning of Business Studies, or at least this was what the students perceived, which set the hypotheses for the study. After conducting the focus group interview, further evidence had to be gathered to support the hypotheses. The data analysis part tried to find out the relationship by correlating the students’ public examination results of Business Studies and levels of English standard. The results indicated a positive correlation between their English standard and Business Studies examination performance. In order to highlight the importance of the English language to the study of Business Studies, the correlation between the public examination results of other non-business subjects was also tested. Statistical results showed that language does play a role in affecting students’ performance in studying Business subjects than the other subjects. The explanation includes the dynamic subject nature, examination format and study requirements, the specialist language used, etc. Unlike Science and Geography, students in their learning process might find it more difficult to relate business concepts or terminologies to their own experience, and there are not many obvious physical or practical activities or visual aids to serve as evidence or experiments. It is well-researched in Hong Kong that English proficiency is a determinant of academic success. Other research studies verified such a notion. For example, research revealed that the more enriched the language experience, the better the cognitive performance in conceptual tasks. The ability to perform this kind of task is particularly important to students taking Business subjects. Another research was carried out in the UK, which was geared towards identifying and analyzing the reasons for underachievement across a cohort of GCSE students taking Business Studies. Results showed that weak language ability was the main barrier to raising students’ performance levels. It seemed that the interview result was successfully triangulated with data findings. Although education failure cannot be restricted to linguistic failure and language is just one of the variables to play in determining academic achievement, it is generally accepted that language does affect students’ academic performance. It is just a matter of extent. This paper provides recommendations for business educators on students’ language training and sheds light on more research possibilities in this area.

Keywords: academic performance, language, learning, medium of instruction

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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