Student Sentiment Research Articles

Planetary Health—Global Environmental Change and Emerging Infectious Disease: A New Undergraduate Online Asynchronous Course

Will a warmer world be a sicker world? What is it about the New England landscape that supports the proliferation of Lyme disease? How are local wildlife trade and global species invasions contributors to emerging diseases such as the 2003 outbreak of monkey pox virus in the midwestern United States? Undergraduate students explore these and related questions in BIOL 1455 Planetary Health: global environmental change and emerging infectious disease—a new online asynchronous course at Brown University. Planetary health is gaining traction in the curricula of institutions of higher learning and online asynchronous courses offer the promise of scaling up to make grand challenges education accessible to many. In our new course, we assessed student learning outcomes (LOs) and sentiment towards the health of humans and the planet using a mixed-methods approach. Students demonstrated competency in each of the LOs after course completion as measured in a pre–post assessment scored with a standardized rubric. Student sentiment was analyzed with an immersion–crystallization qualitative analysis to elucidate themes in responses to the assessments. Many themes on the pre-assessment focused on barriers and problems associated with the health of humans and the planet, while themes on the post-assessment centered on more solutions-based thinking. Collectively, these findings indicate that this online asynchronous course successfully educated students about the myriad challenges facing human and planetary health, broadened knowledge of environmental changes (beyond climate change) that impact health, formalized understanding of now common terms such as “emerging infectious diseases”, and bolstered hope by offering solutions and peer community (even when virtual). Future efforts to integrate planetary health into higher education should focus on broadly accessible and scalable courses, full programs of study (i.e., majors/scholarly concentrations), and extension into institutional programs focused on ensuring equity and wellness for all.

The Sentiments of Students and Teachers on Modular Distance Learning Amid the Health Crisis

Due to the COVID-19 pandemic, schools, particularly in rural areas, employed Modular Distance Learning (MDL) to ensure educational continuity. Modular distance learning is the current learning modality of primary education, where parents serve as parent-teachers to their children. This study seeks to evaluate the experiences of students and teachers of Elementary School, on modular distance learning during the pandemic. This study used the qualitative method of interviewing nine students and six teachers to learn about their MDL experiences. Data process involves combining related concepts and themes to produce a more structured and organized picture of the data. MDL strengthens family bonding, promotes independent learning, and economizes money and time. However, it is an additional workload for working parents; there needs to be more teacher-student interaction, preventing pupils from socializing and gadget distractions. The article revealed that MDL has positive and negative experiences for teachers and students. Therefore, the impact may vary depending on individual circumstances and adaptability. The study suggests that suitable strategies should address any challenges during implementation and evaluation. Furthermore, teachers must undergo training related to MDL to address existing problems in delivering their lessons.

Deep learning for opinion mining and topic classification of course reviews

AbstractStudent opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volumes of comments at institution level or online forums. In this paper, we collected and pre-processed a large number of course reviews publicly available online. We applied machine learning techniques with the goal to gain insight into student sentiments and topics. Specifically, we utilized current Natural Language Processing (NLP) techniques, such as word embeddings and deep neural networks, and state-of-the-art BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly optimized BERT approach) and XLNet (Generalized Auto-regression Pre-training). We performed extensive experimentation to compare these techniques versus traditional approaches. This comparative study demonstrates how to apply modern machine learning approaches for sentiment polarity extraction and topic-based classification utilizing course feedback. For sentiment polarity, the top model was RoBERTa with 95.5% accuracy and 84.7% F1-macro, while for topic classification, an SVM (Support Vector Machine) was the top classifier with 79.8% accuracy and 80.6% F1-macro. We also provided an in-depth exploration of the effect of certain hyperparameters on the model performance and discussed our observations. These findings can be used by institutions and course providers as a guide for analyzing their own course feedback using NLP models towards self-evaluation and improvement.

NAGNet: A novel framework for real‐time students' sentiment analysis in the wisdom classroom

SummaryFacial expression is the basis of human emotion recognition, and we can infer the emotional state of human beings by analysing facial expressions. As the main method of human emotional expression, facial expression contains much information about inner emotional changes. In recent years, with the wide application of artificial intelligence in education, related research on student sentiment analysis has become a hot topic in the educational technology field. Analysing affective data is helpful for understanding students' learning status and provides an important basis for the effective implementation of learning interventions, which is of great significance for the evaluation of teaching effects and changes in teaching methods. However, traditional learner emotion recognition methods have some problems, such as low recognition rates, complex algorithms, poor robustness, and easy loss of the key information of facial expression features. Therefore, this article proposes a learner emotion recognition method based on NAGNet. The network model is composed of Res2Net, nonlocal attention and GeM pooling, which can fuse global expression feature information to realize fine‐grained sentiment analysis. Additionally, we conducted training and experiments on the large‐scale learner emotion dataset FERPlus. The NAGNet model trained by the public emotion dataset FERPlus has a recognition accuracy of 89.3% for eight kinds of student emotions. The experimental results show that the method can quickly and accurately identify learner emotional states. We conduct experiments in a real classroom scenario, and use the NAGNet model proposed in this article to analyse and detect students' real‐time sentiment. Then teachers improve teaching methods by understanding the feedback of students' classroom status. Therefore, this method has important reference significance in the construction of wisdom classrooms and wisdom learning environments.

SENTIMENT ANALYSIS OF STUDENTS’ FEEDBACK ON INSTITUTIONAL FACILITIES USING TEXT-BASED CLASSIFICATION AND NATURAL LANGUAGE PROCESSING (NLP)

In this study, sentiment analysis, commonly referred to as opinion mining or emotional artificial intelligence (AI), is used to obtain student comments about institutional facilities. To ascertain whether an online text has a good, negative, or neutral emotional tone, it must first be analyzed. Natural Language Processing (NLP) includes the subfield of sentiment analysis, and NLP can be used to categorize and extract information with the aid of machine learning methods. Finding out if students are content with the amenities or services provided is important since, in an educational setting, they are consumers. This study of the resources and services that the libraries offer evaluated college students' perceptions of the books, audio CDs, and video CDs, the services provided by the library staff, and the personal computers that are made available to them as part of their facilities. In the current study, surveys were carried out to gather information and assess how well the needs of the students are addressed. On Kaggle.com, secondary data from a North Indian institution was used in the experiments. The research approach was sentiment analysis using a machine learning framework. The F1-score, a harmonic mean of precision and recall based on the attitudes evaluated by the algorithms, was calculated using a text-based classification method using Naive Bayes Multinomial and Support Vector machine learning algorithms. The findings offer views on how successfully library facilities are managed in the form of student sentiments.

Analisis Sentimen Mahasiswa Terhadap Perkuliahan Dalam Jaringan Menggunakan Metode Naïve Bayes Classifier

Since the pandemic of Covid-19 was happened in Indonesia, the government shared the letter of The Ministry of Education and Higher Culture Education Directorate No.1, year 2020 about prevention of the spread of Corona Virus Disease (Covid-19) in higher education. Through the letter, The Ministry of Education and Culture gave an instruction for college to organize online learning and suggested students to study at their home. Online learning which was considered as a strategy then became a controversy because it needed adaption. This sudden change from normal learning to online learning caused many responses from students. The aim of this research was to analyze student sentiment or responses on online learning in this pandemic era of Covid-19 in Indonesia by using data which had been collected using questionnaire and processed using naïve bayes classifier method. This research was case study descriptive quantitative research. The research was done by collecting the data first. The data was collected through questionnaire with the question about their opinion on online learning in this pandemic Covid-19 era. The data was 157 student’s data opinion on online learning. After the data was collected, the data was cleaned first from question mark and the words which didn’t give an effect in sentiment analysis. After the data was cleaned, then the result of the classification will be showed as well as the accuracy which the model earned. The result showed that online learning had negative sentiment more than positive sentiment. The height of negative sentiment was caused by discomfort of student in online learning. The word which frequently showed was ‘tidak efektif’, ‘susah’, and ‘tugas’. The accuracy of this model was 75% when the result of this accuracy was good result in classification.

Connect with Student-Educational Consumer Brand Management Engagement: A Social Media Management Perspective

Social media empowers individuals to express their views regarding the events that take place in their day-to-day life. These events also incorporate the sentiments of students as well as educators towards their educational institutions. Thus, the study aims to analyze the sentiments of the students regarding their educational institutions to hike consumer brand management. However, Understanding the student at all levels of his being (physical, mental, emotional spiritual) is crucial to generate valuable learning and bringing forth a sense of belongingness. The study proposes to use social media platforms to connect, understand and engage the students at the professional as well as personal levels. Design/methodology/approach: Sentiment Analysis using vectorization and polarity has been applied to data collected from tweets of several leading universities of the world with their social media appearances to acquire significant insights into the objective of the study. During the analysis, it has been observed that symbol such as #hashtags and @ has played an intensified role in arousing the emotions of the individuals which eventually contributed to the branding of a particular designated university. Findings: Sentiment and emotional identification play a significant role in increasing brand value and nurturing the relationship between the student and the educator. It has been observed from the study that a sense of intensified community building is promoted which encourages new individuals to become a part of the reputed universities. Research Limitation/Implication: Since the field of education plays a great important role in the policies and economy of every country, students are the driving factors of this sector. Hence it is important to identify the emotion and sentiments of an individual regarding each small role that he goes through while studying. The university plays a very important role thus the study focuses on how each activity in the university affects the sentiments of a student and at the same time, it targets the nurturing of the student-educator relationship. The study focuses on how distinctive activities at the university level, their implication, and engagement at social media platforms affect the student's sentiments. The study is limited to the student-educator relationship. The present study contributes to the increase in consumer brand participation through emotion analysis and natural language processing methodology. Practical Implication: It refers to the fulfillment of certain conditions that if gets fulfilled will change reality to some extent. In this study, we have incorporated different tweets collected from several top universities across the world, since the data is in the form of tweets it has been processed to be subjected to analytical purposes. The practical implication for the study lies in the collection of datasets as the core of the study is the tweets that form the dataset. Social Implication: The social implication of the study is the representation of the sentiment of an individual regarding his or her university activities (Quality of Education, Research activities, etc.). After the analysis, there is a sense of pride has been observed among the student regarding the achievements of their educators and university. This not only inculcates a sense of belongingness among a group but also leads to the engagement of the student-educator relationship which further nurtures the policies of the educational universities to increase the brand value and consumer engagement. Originality/value: Engaging students on social media platforms, not only urge them to pursue it as a crucial medium of information but also bring a sense of pride to be part of their university. The study's findings not only contribute to the current understanding of sentiment analysis in brand engagement but are also important to develop a sense of belongingness in the students towards their educational institution which further nurtures the student-educator relationship management.

Sentiment Analysis of Online Lectures using K-Nearest Neighbors based on Feature Selection

Online lecture is a distance learning system that utilizes information technology in its implementation. Although it has been agreed, this lecture system has caused controversy. Not infrequently online lectures are considered to bring a variety of new obstacles in lectures, and not a few also consider that online lectures are the most appropriate solution to continue to run lecture activities in the midst of alarming pandemic conditions. In response to this policy, many people expressed various kinds of opinions and views on the implementation of online lectures which are generally stated on social media, one of which is through Twitter. Sentiment analysis is a branch of the science of machine learning that is carried out to obtain useful information or new knowledge by extracting, understanding, and processing text data automatically. Several methods are widely used by researchers to classify sentiment analysis datasets including K-Nearest Neighbor (K-NN). K-NN will be adapted to classify online lecture datasets because K-NN can produce good accuracy on a large number of data. The presence of feature selection also helps machine learning in improving its performance. The purpose of this study was to determine student sentiment toward online lectures and to determine the level of accuracy of the combination of K-NN with various feature selections. Based on 780 tweets data, a classification of 394 positive sentiments, 320 negative sentiments, and 39 neutral sentiments was obtained. So, the results of student opinions are on POSITIVE sentiments. The accuracy result of the K-NN Algorithm was 56% and the accuracy of the K-NN Algorithm + Forward Selection was 51%, the accuracy of the KNN Algorithm + Adabost was 54%, and the accuracy of the KNN Algorithm + Genetic Algorithm was 55%.

SATISFACTION WITH THE EDUCATIONAL PROCESS AS A FACTOR OF PROTEST ATTITUDES OF STUDENT YOUTH

Education is the main area of youth employment. It can be the main source of personal and group discontent. Analyzing the content of protest sentiments of students as their reaction to the ongoing events in the field of education, it is possible to predict and prevent protest behavior, as well as assess the quality of the educational environment. The empirical study included the collection and analysis of data obtained through questionnaires (n=608), in-depth interviews (n=12) and focus groups (n=10). The study identified the main indicators of satisfaction with the educational process of students, their attitude to distance learning, as well as problems that can provoke protest mood.

A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network.

The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of the pressing issues facing college administrators right now is finding a method that is both scientific and useful for determining the mental health of college students. In pace with the advancement of Internet technology, the Internet has become an important communication channel for contemporary college students. As one of the main forces in the huge Internet population, college students are at the stage of growing knowledge and being most enthusiastic about new things, and they like to express their opinions and views on study life and social issues and are brave to express their emotions. These subjective text data often contain some affective tendencies and psychological characteristics of college students, and it is beneficial to dig out their affective tendencies to further understand what they think and expect and to grasp their mental health as early as possible. In order to address the issue of assessing the mental health of college students, this study makes an effort to use public opinion data from the university network and suggests a college student sentiment analysis model based on the OCC affective cognitive model and Bi-LSTM neural network. In order to label three different types of positive, negative, and neutral sentiment on the microblog text of college network public opinion, we first design a sentiment rule system based on the OCC affective cognition elicitation mechanism. In order to effectively and automatically identify the sentiment state of college students in the network public opinion, this study uses a Bi-LSTM neural network to classify the preprocessed college network public opinion data. Finally, this study performs comparison experiments to confirm the validity of the Bi-LSTM neural network sentiment recognition algorithm and the accuracy of the OCC sentiment rule labeling system. The findings show that the college student sentiment recognition effect of the model is significantly enhanced when the OCC sentiment rule system is used to label the college network public opinion data set as opposed to the naturally labeled data set. In contrast to SVM and other classification models like CNN and LSTM, the Bi-LSTM neural network-based classification model achieves more satisfactory classification results in the recognition of college opinion sentiment.

Mining opinions on LMOOCs: Sentiment and content analyses of Chinese students’ comments in discussion forums

The rapid rise of Massive Open Online Courses (MOOCs) has attracted intense discussion on their benefits to education transformation and related challenges. Language MOOCs (LMOOCs) as an emerging field requires more empirical attention, particularly in terms of learners' perceptions and needs regarding LMOOC learning. This study aimed to explore LMOOC students' sentiments and opinions through sentiment analysis and content analysis of their comments posted in the discussion forums of 60 LMOOCs and examine the correlation between student sentiment and course rating. The results indicated that the majority of the comments were positive, and student sentiment was positively correlated with course rating. Five major themes that encapsulated students' opinions about LMOOCs and their experiences in LMOOC learning were identified: attitudes towards the LMOOCs, comments on the LMOOCs, evaluations of LMOOC instruction and instructors, learning outcomes, and suggestions. To shed light on LMOOC development, the LMOOC learners’ concerns and suggestions were particularly examined, and tactics possibly used by learners to express euphemistically or circumvent negative feedback were speculated. Implications for LMOOC instructors and developers and future research are finally presented.

Analysis of student sentiment during video class with multi-layer deep learning approach

<p>The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.</p>

Development of IFS (Internal Family Systems) Program to Improve Adolescent Self-Identification: Put emphasis on the focus group of student sentiment and behavioral characteristics tests

Background: IFS treatment has been developed by Schwartz, Based on the fact that it forms a stable emotion through the encounter of self, we would like to help adolescents classified as focus groups identified by the test of the student’s emotional and behavioral characteristics. Purpose: By developing a program that improves the stable growth and positive self-identity for adolescents, we tried to obtain the positive effect in the adolescences coping with the crisis. Methodology/Approach: As detailed goals as follows; (1) Observe the characteristics of adolescence and develop the programs by IFS access. (2) When the program was developed for the improvement of self-identity, it was included object, goal, composition, and content as essential factors. (3) Both preliminary research and evaluation were preceded to develop practical IFS programs to improve adolescents' self-identity. (4) The effects of the programs were verified through pre- and post-quantitative and qualitative evaluation of the self-identity, IFS, student sentiment, and the scales of behavioral characteristic test. Findings/Conclusions: The overall results of the study show that the content of the IFS program for assisting the self-identification for youth has produced significant outcomes. Furthermore, the IFS treatment program was verified to assist in the self-identification of youths, the improvement in the IFS (Internal Family System), and reduction in emotional and behavioral problems. Implications: The results of this study are as follows; (1) The focus group from the student sentiment and behavioral characteristic test and adolescents were subjected to the study. (2) It derives the natural and active participation of the interest group of adolescents. (3) The study was conducted in parallel with quantitative effect verification and qualitative analysis. (4) IFS program was successfully constructed to improve adolescents' self-identity.

Using Reinforcement Learning for Operating Educational Campuses Safely during a Pandemic (Student Abstract)

The COVID-19 pandemic has brought a significant disruption not only on how schools operate but also affected student sentiments on learning and adoption to different learning strategies. We propose CampusPandemicPlanR, a reinforcement learning-based simulation tool that could be applied to suggest to campus operators how many students from each course to allow on a campus classroom each week. The tool aims to strike a balance between the conflicting goals of keeping students from getting infected, on one hand, and allowing more students to come into campus to allow them to benefit from in-person classes, on the other. Our preliminary results show that reinforcement learning is able to learn better policies over iterations, and that different Pareto-optimal tradeoffs between these conflicting goals could be obtained by varying the reward weight parameter.

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

AbstractSentiment analysis has a wide application prospect in business, medicine, security and other fields, which provides a new perspective for the development of education. Students' sentiment data play an important role in the evaluation of teachers' teaching quality and students' learning effect, and provide a basis for the implementation of effective learning intervention. However, most of the research is to obtain the real‐time learning status of students in the classroom through teachers' naked eye observation and students' text feedback, which will lead to some problems such as incomplete feedback content and delayed feedback analysis. Based on the mini‐Xception framework, this article implements the real‐time identification and analysis of student sentiment in classroom teaching, and the degree of student engagement is analyzed according to the teaching events triggered by teacher to provide reasonable suggestions for subsequent teaching progress. The experimental results show that the mini‐Xception model trained by FER2013 data sets has high recognition accuracy for the real‐time detection of seven student sentiments, and the average accuracy is 76.71%. Compared with text feedback, it can assist teachers in understanding student learning states in time so that they can take corresponding actions, and realize the real‐time performance of wisdom classroom teaching information feedback, the high efficiency of information transmission, and the intelligence of information processing.

Comparative analysis of machine learning approaches for sentiment analysis of students' online learning feedback during covid-19

Sentiment analysis aids in determining if a person's feelings are neutral, negative, or positive. Many machine learning and deep learning algorithms exist for assessing people's attitudes on various social media networks. Many researchers focused on students' emotional identification. The purpose of this paper is to analyze the sentiments of academic students regarding the online class experience conducted during the COVID-19 pandemic situation. For this work, the Term Frequency-Inverse Document Frequency (TF-IDF) model is used for the feature extraction and comparison of eight machine learning models were tested for the classification, such as Support Vector Classifier, Multinomial Naïve Bayes, Decision Tree, K-Nearest-Neighbors (KNN), Random Forest, AdaBoost Classifier, Bagging Classifier, Extreme Gradient Boosting Classifier (XGB) and F-Score, accuracy, precision, and Recall are the performance criteria examined. With a test accuracy of 0.97 and precision of 1.0, Multinomial Naive Bayes achieves the highest accurate model.

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Sentiment analysis attracts the attention of Egyptian Decision-makers in the education sector. It offers a viable method to assess education quality services based on the students’ feedback as well as that provides an understanding of their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. After dataset preprocessing to remove symbols, necessary stemming and lemmatization is performed for features extraction. This is followed by several machine learning techniques and a proposed Long Short-Term Memory (LSTM) classifier optimized by the Salp Swarm Algorithm (SSA) and measured the corresponding performance. Then, the validity and accuracy of commonly used classifiers, such as Support Vector Machine, Logistic Regression Classifier, and Naive Bayes classifier, were reviewed. Moreover, LSTM based on the SSA classification model was compared with Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB). Finally, as LSTM based SSA achieved the highest accuracy, it was applied to predict the sentiments of students’ feedback and evaluate their association with the course outcome evaluations for education quality purposes.

Fuzzy rule based sentiment analysis for finding University Student Satisfaction in Yemen

Objectives: To analyze the sentiment and understand Student’s opinions and satisfaction about university, and the services which introduce by it to reach accuracy in evaluation of the university. Methods: The sentiment of students was analyzed and quantified the satisfaction towards the University by building an algorithm using a set of fuzzy rules. This study proposes an integrated optimization method: applying a set of fuzzy rules with two lexicons (SentiWordNet and AFINN lexicon) besides to other parameter. The lexicons in sentiment anlysis are individual words that can be considered as a unit of opinion information. The proposed fuzzy system integrates Natural Language Processing techniques to analyze and quantify student’s satisfaction towards the University by classify the comments into very positive, positive, very negative, negative, or neutral sentiment classes. Findings: with this approach, the accuracy is more 0.891 compared to Support Vector Machines (SVM), Naïve Bayes, The Fuzzy Inference to analyze the sentiment with their own lexicon Opinion Words Lexicon and classified the sentiment into 2 classpositive, negative and The Fuzzy Inference to analyze the sentiment with only SentiWordNet lexiconin evaluation obtained for University evaluation, the evaluation has been verified by simulation results on MATLAB. Novelty: The novelty of this study lies in the formulation of few fuzzy rules to evaluate the sentiment class of tweets, and the proposed model can be adapted to any lexicon. Keywords: Sentiment analysis; Fuzzy rule; linguistic variables; Students comments analysis

Sentiment Analysis of Student Texts Using the CNN-BiGRU-AT Model

For most current sentiment analysis models, it is difficult to capture the complex semantic and grammatical information in the text, and they are not fully applicable to the analysis of student sentiments. A novel student text sentiment analysis model using the convolutional neural network with the bidirectional gated recurrent unit and an attention mechanism, called CNN-BiGRU-AT model, is proposed. Firstly, the text is divided into multiple sentences, and the convolutional neural network (CNN) is used to extract n-gram information of different granularities from each sentence to construct a sentence-level feature representation. Then, the sentences are sequentially integrated through the bidirectional gated recurrent unit (BiGRU) to extract the contextual semantic information features of the text. Finally, an attention mechanism is added to the CNN-BiGRU model, and different learning weights are applied to the model by calculating the attention score. The top-down text features of “word-sentence-text” are input into the softmax classifier to realize sentiment classification. Based on the weibo_senti_100 k dataset, the proposed model is experimentally demonstrated. The results show that the accuracy rate and recall rate of its classification mostly exceed 0.9, and the F1 value is not lower than 0.8, which are better than the results of other models. The proposed model can provide a certain reference for the related students’ text sentiment analysis research.

Subjectivities in curricular practices in Mathematics in Youth and Adult Education

Background: The curriculum practices, especially those related to Mathematics, developed in EJA, can exert a strong influence on the students' sentiment of self-efficacy and, in turn, influence the cognitive processing of mathematical knowledge. Objective: To investigate which meanings of curricular practices in Mathematics are subjectived by EJA teachers. Design: Narratives of 5 teachers who work at EJA, whose data collection was through interviews. Environmente and participants: The research took place in a rural municipal public school located in a city in the interior of Ceará. Data collection and analysis: Textual Discursive Analysis. Results: The curricular practices developed in EJA, especially those related to mathematics, can exert a strong influence on the student's sentiment of self-efficacy and influence their cognitive processing of mathematical knowledge. The moments of collective pedagogical planning and training influence the selection of curriculum content and teaching materials, guiding the construction of the teachers' curricular practices, influencing the construction of their subjectivities, in a permanent relationship between subjects who interact and confront each other from different ways for their pedagogical work, including the support they seek from their peers. Conclusions: Thinking about the subjectivities of the actors involved in EJA should mean turning the attention to the teaching and learning processes, which encompass a range of diversities of social groups with very different cultures, expectations, ages and interests, meaning greater attention to production and implementation of appropriate curricula and teaching for these groups of students, considering their cultures, experiences and previous experiences.