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This paper aims to address the need to ensure the quality of metadata records describing learning resources. We propose improvements to a metadata-quality model, specifically for the compliance sub-feature of the functionality feature. Compliance is defined as adherence level of the learning object metadata content to the metadata standard used for its specification. The paper proposes metrics to assess the compliance, which are applied to a set of learning objects, showing their applicability and usefulness in activities related to resources management.

The methodology considers a first stage of metrics refinement to obtain the indicator of the sub-feature compliance. The next stage is the proposal evaluation, where it is determined if metrics can be used as a conformity indicator of learning object metadata with a standard (metadata compliance). The usefulness of this indicator in the information retrieval area is approached through an assessment of learning objects where the quality level of its metadata and the ranking in which they are retrieved by a repository are correlated.

This study confirmed that the best results for metrics of standardization, completeness, congruence, coherence, correctness and understandability, which determine the compliance indicator, were obtained for learning objects whose metadata were better labelled. Moreover, it was found that the learning objects with the highest level of compliance indicator have better positions in the ranking when a repository retrieves them through an exact search based on metadata.

In this study, only a sub-feature of the quality model is detailed, specifically the compliance of learning object standard. Another limitation was the size of the learning objects set used in the experiment.

This proposal is independent from any metadata standard and can be applied to improve processes associated with the management of learning objects in a repository-like retrieval and recommendation.

The originality and value of this proposal are related to quality of learning object metadata considered from a holistic point of view through six metrics. These metrics quantify both technical and pedagogical aspects through automatic evaluation and supported by experts. In addition, the applicability of the indicator in recovery systems is shown, by example to be incorporated as an additional criterion in the learning object ranking.

Social presence (SP), which refers to individuals’ perception of others being engaged as “real people” in the same situation, is a crucial component in technology-rich learning environments (TREs). This study aims to identify major learning design, antecedents and outcomes of SP within TREs, and identify common findings from the past two decades.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses review principles and a qualitative analysis of selected articles, a final review of 72 studies that met inclusion criteria was obtained. Key information, including education level, discipline, sample size, study type and measurements, was extracted and studies were further analyzed and synthesized based on design features and learning modes.

The study identifies five crucial factors for instructional design to foster SP in TREs: technology affordances, multimedia features, social factors, instructional principles, learner characteristics and learning management systems. The authors compare two learning modes across three dimensions and identify popular technologies used in studies related to SP over the past two decades. Practical recommendations are provided for educators and educational technology developers to enhance SP within technology-rich learning environments.

This research contributes to the discourse on online learning and computer-supported communication, particularly in the post-COVID-19 era. By examining factors influencing SP and providing implications for instruction and educational technology development, this study offers evidence-based support to educators for engaging learners and fostering authentic learning experiences through adaptive selection of educational technologies.

This paper aims to describe the process used to create an emotion lexicon enriched with the emotional intensity of words and focuses on improving the emotion analysis process in texts.

The process includes setting, preparation and labelling stages. In the first stage, a lexicon is selected. It must include a translation to the target language and labelling according to Plutchik’s eight emotions. The second stage starts with the validation of the translations. Then, it is expanded with the synonyms of the emotion synsets of each word. In the labelling stage, the similarity of words is calculated and displayed using WordNet similarity.

The authors’ approach shows better performance to identification of the predominant emotion for the selected corpus. The most relevant is the improvement obtained in the results of the emotion analysis in a hybrid approach compared to the results obtained in a purist approach.

The proposed lexicon can still be enriched by incorporating elements such as emojis, idioms and colloquial expressions.

This work is part of a research project that aids in solving problems in a digital society, such as detecting cyberbullying, abusive language and gender violence in texts or exercising parental control. Detection of depressive states in young people and children is added.

This semi-automatic process can be applied to any language to generate an emotion lexicon. This resource will be available in a software tool that implements a crowdsourcing strategy allowing the intensity to be re-labelled and new words to be automatically incorporated into the lexicon.

This paper aims to propose a method for automatically labelling an affective lexicon with intensity values by using the WordNet Similarity (WS) software package with the purpose of improving the results of an affective analysis process, which is relevant to interpreting the textual information that is available in social networks. The hypothesis states that it is possible to improve affective analysis by using a lexicon that is enriched with the intensity values obtained from similarity metrics. Encouraging results were obtained when an affective analysis based on a labelled lexicon was compared with that based on another lexicon without intensity values.

The authors propose a method for the automatic extraction of the affective intensity values of words using the similarity metrics implemented in WS. First, the intensity values were calculated for words having an affective root in WordNet. Then, to evaluate the effectiveness of the proposal, the results of the affective analysis based on a labelled lexicon were compared to the results of an analysis with and without affective intensity values.

The main contribution of this research is a method for the automatic extraction of the intensity values of affective words used to enrich a lexicon compared with the manual labelling process. The results obtained from the affective analysis with the new lexicon are encouraging, as they provide a better performance than those achieved using a lexicon without affective intensity values.

Given the restrictions for calculating the similarity between two words, the lexicon labelled with intensity values is a subset of the original lexicon, which means that a large proportion of the words in the corpus are not labelled in the new lexicon.

The practical implications of this work include providing tools to improve the analysis of the feelings of the users of social networks. In particular, it is of interest to provide an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyberbullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.

This work is interested in providing an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyber bullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.

The originality of the research lies in the proposed method for automatically labelling the words of an affective lexicon with intensity values by using WS. To date, a lexicon labelled with intensity values has been constructed using the opinions of experts, but that method is more expensive and requires more time than other existing methods. On the other hand, the new method developed herein is applicable to larger lexicons, requires less time and facilitates automatic updating.

This paper aims to show the results obtained from the data mining techniques application to learning objects (LO) metadata.

A general review of the literature was carried out. The authors gathered and pre‐processed the data, and then analyzed the results of data mining techniques applied upon the LO metadata.

It is possible to extract new knowledge based on learning objects stored in repositories. For example it is possible to identify distinctive features and group learning objects according to them. Semantic relationships can also be found among the attributes that describe learning objects.

In the first section, four test repositories are included for case study. In the second section, the analysis is focused on the most complete repository from the pedagogical point of view.

Many publications report results of analysis on repositories mainly focused on the number, evolution and growth of the learning objects. But, there is a shortage of research using data mining techniques oriented to extract new semantic knowledge based on learning objects metadata.

This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface.

The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades.

On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively.

This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

The integral approach to risk is currently an important background for the local development processes within the sustaintability framework. Given the greater frequency and intensity of extreme climatic events in the period 2005-2015, whose inventory shows a total of 78 extreme climate events in the four thermal stations, the purpose of this paper is to analyze risk situations and their corresponding management in the tourist destinations (TD) of the Atlantic coast of Buenos Aires, Argentina.

Risk management in TDs has not been addressed in depth in the study area. Because of that, this study is exploratory and descriptive, and it was conducted by quantitative and qualitative methods. The occurrence of extreme weather events was the trigger of this study. Thereafter, the existence of risk situations was analyzed from the combination of natural hazards and material and human damage. The actions taken by the public sector were identified to make a diagnosis of the current management of coastal destinations in the province of Buenos Aires.

The result of this study shows that TDs do not apply integral models of risk management, as only 16% of the total registered events were attended by public management from reactive measures to the event. It is believed possible to replicate this study in other TDs, regardless of its main tourism modality, as the analysis carried out in the coastal destinations of Buenos Aires shows that it is necessary to plan and manage risk to avoid and/or mitigate material, social and economic damages of the resident and tourist population.

The main implication of the study, in practical terms, is associated with the incorporation of specific equipment for the detection of storms and other meteorological phenomena. In addition, it should start, from the management, with a process of awareness of the resident and tourist population about the risk and its consequences.

Knowing the existence of natural hazards is a strategic resource for public management. From the identification of the hazards, exposure and vulnerability of the population, it is possible to begin to take measures to mitigate the risk and conduct awareness campaigns about the risk situations that may arise from the occurrence of meteorological phenomena. in beach areas. In this sense, the development of a culture of risk is very important.

The relationship between the occurrence of extreme weather events and tourism has not been addressed in depth in Argentina. Therefore, this work provides an interdisciplinary vision (from tourism and physical geography) about the hazards that extreme phenomena represent in TDs, the situations of risk that they detaches there and the weakness of public management in coastal destinations of Buenos Aires. This case study shows that the absence of planning and risk management has serious implications for the continuity of the development processes of the destinations.

The purpose of this paper is to identify a useful taxonomy of frugal innovation (FI) applied to women-led family businesses in rural communities in Oaxaca, Mexico when confronted with disruptive situations such as the recent COVID-19 pandemic, thus achieving a greater understanding of FI in this context to provide these businesses with strategies that allow them to face crises more effectively.

The study was cross-sectional and quantitative. A scale was developed and applied to 160 family businesses run by women and located in four rural communities in Oaxaca, Mexico. A review of the literature from the period of 2018–2024 made it possible to identify the essential characteristics of FI.

Through an exploratory factorial analysis, four types of FIs were identified: new production and marketing models, new methods of operation, new financing methods and new organizational methods. Using a discriminant analysis to establish the functionality of the identified FIs, the authors found that shifting to new financing and organizational methods was more important for the survival of family businesses in times of crisis.

This study highlights the forms of FI that develop in small family businesses led by women; this is important for the survival of the family and the business. The research highlights innovation challenges and opportunities for women entrepreneurs in the global south.

The purpose of this paper is to identify and describe the pervasiveness and importance of various types of institutional and organizational interactions across multiple levels for the management of a community forest enterprise.

The paper analyzes a long‐standing case in Michoacán, Mexico, the San Juan Nuevo (SJN) enterprise, a community‐based system with a multiplicity of actors, objectives, and partners. Information was collected through 100 semi‐structured interviews. By presenting and discussing the main community‐based development strategy within the overall socio‐political context and achievements of the case, the authors attempt to understand the complexity of cross‐scale institutional and organizational linkages and their role in sustainable resource management.

SJN enterprise had linkages with some 22 major partners over the years across four levels of organization: local, state, federal, and international. Cross‐scale partnerships were not merely important, but essential for the overall success of the enterprise in the face of uncertainty over resource ownership and lack of legal jurisdiction. These diverse partnerships and interactions enabled robust institutional structures, making possible the development of linkages to help conserve the resource base and create grassroots socio‐economic development for the comuneros.

Further understanding of the importance of partnerships and linkages for the development and maintenance of community‐based initiatives will require the analysis of, and comparison between, several long‐standing case studies.

There is the need to recognize the multiple roles of partnerships, from business networking to research and training, thus unpacking different kinds of capacity building. Actors at various levels can influence management practices in diverse ways, helping to find a balance between local livelihoods and larger conservation needs.

The paper brings a new approach to analyze how indigenous and other rural communities are “opting‐in” to the global economy, through a diversity of partnerships and a complexity of interactions across organizational levels.

This study aims to identify how Spanish consumers’ extrinsic preferences for food have evolved by examining the extant literature on food preferences in Spain, focusing on food-related attributes and food-related values.

This study is based on a synthetic review of the extant academic literature on Spanish consumer preferences for food-related attributes and food-related values from the mid-20th to the 21st century. This study uses key economic and social milestones that are most likely to influence food value chain actors to show how consumer preferences have evolved over the study period.

Spanish consumer food attribute preferences expanded as the food sector of the nation continued to grow, and value preferences showed a similar pattern from the mid-20th to the 21st century. The drivers of these preferences were trust, lifestyle, education (campaigns), sociodemographic factors and purchasing power.

Evaluating the extant literature’s contribution to consumer preferences for food-related attributes and values is important because it can aid in understanding the hierarchy and variety of consumers’ food preferences as well as the factors that drive these preferences. To the best of the authors’ knowledge, this study is the first to explore how Spanish consumer preferences evolved between the mid-20th and 21st centuries.