Learning descriptive text interactively: Combining the students’ creativity and writing skills for Phase D

With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be of different types, making the fusion of data non-trivial. Moreover, the devices are often mobile, resulting in that data from a particular sensor is not always available, i.e. there is a need to handle data from a dynamic set of sensors. From a machine learning perspective, the data from the sensors arrives in a streaming fashion, i.e., online learning, as compared to many learning problems where a static dataset is assumed. Machine learning is in many cases a good approach for classification problems, but the performance is often linked to the quality of the data. Having a good data set to train a model can be an issue in general, due to the often costly process of annotating the data. With dynamic and heterogeneous data, annotation can be even more problematic, because of the ever-changing environment. This means that there might not be any, or a very small amount of, annotated data to train the model on at the start of learning, often referred to as the cold start problem. To be able to handle these issues, adaptive systems are needed. With adaptive we mean that the model is not static over time, but is updated if there for instance is a change in the environment. By including human-in-the-loop during the learning process, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used is typically annotations of the data, i.e. user input in the form of correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy. In this thesis we explore which interactive learning strategies are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on performance. We also study how a user who is not always reliable, i.e. that does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when previous estimations have been incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data.

The integration of interactive web-based learning media is essential to address challenges in teaching hydrocarbon materials at SMAN 1 Sungai Pinang. Traditional teaching methods, while effective, often lack student engagement, motivation, and interactivity. This study explores the need for digital learning tools to enhance chemistry education. A needs analysis was conducted through surveys and questionnaires to assess student and teacher perceptions regarding existing teaching methods. The validity and reliability of the questionnaire were confirmed to ensure accurate data collection. The study also examined school facilities and academic challenges that impact the implementation of interactive learning. Findings indicate that well-equipped school facilities support the adoption of interactive and digital learning strategies. While traditional methods remain beneficial, a blended learning approach is necessary to optimize student engagement and academic performance. The study also highlights various academic and non-academic challenges faced by students, necessitating further support and development. Regular evaluation of teaching strategies is crucial to adapting to students' evolving needs. The study underscores the importance of interactive web-based learning media in improving teaching effectiveness and student engagement. The findings suggest that supplementing traditional methods with digital tools can enhance comprehension and motivation. This study supports the development of interactive digital learning media to complement traditional chemistry instruction. Implementing blended learning strategies can address current educational challenges, fostering a more engaging and effective learning environment.

This research aims to evaluate the use of the spinning top method in improving students' ability to understand physics concepts. The spinning top method, which involves active and interactive learning through physical demonstrations, is used as an alternative approach in teaching physics. This research involved students as research subjects and was carried out by comparing learning outcomes between the group that received training using the gasing method and the control group that used the conventional method. Data was collected through physics concept understanding tests before and after the intervention, as well as through classroom observations. Data analysis was carried out to evaluate the effectiveness of the spinning top method in increasing students' understanding of physics concepts. It is hoped that the research results can provide valuable insight for educational practitioners in developing more interactive and effective learning strategies in the context of physics learning.

The Interactive Teaching Project, described in this paper, was designed to test an instructional model designed to help students identified as learning disabled comprehend and learn content area concepts. Reported is a description of the theoretical rationale for the model, a description of the teaching and learning strategies used, and the effects of those strategies. The conclusions are based on data collected over three years in elementary bilingual social studies and junior high science classrooms. Overall, the interactive strategies (semantic feature analysis, semantic mapping, and semantic/syntactic feature analysis) were found to be more effective than instruction emphasizing definitions for students striving to learn content area concepts.

This study investigates students' writing ability, both before and after, and the significant difference after using the Interactive Learning Strategy. This study is categorized as pre-experimental, involving one class as a treatment class. For this study, the sample was collected through a purposive sampling technique, which consisted of sixteen students. Eighth-grade students at SMP Negeri 4 Pulau Merbau were involved in this study. Data were analyzed using SPSS version 23. Researchers applied the Wilcoxon signed-rank test to evaluate data. Results showed an extensive difference in the writing skill of students before and after applying the Interactive Learning Strategy, with pre-test gain of 6.25% and post-test gain of 68.0%. Based on the Wilcoxon signed-rank test, it indicates that Ha is acceptable since sig (2-tailed) = 0.000. The decision-making ground for Ha is significant (sig < 0.05), while H0 is significant (sig > 0.05).

In order to cope with the multidimensional non-linear optimization problems which involved a great number of discrete variables and continuous variables, a self-organizing learning algorithm (SOLA) was proposed in this paper, in which the parallel search strategy of genetic algorithm(GA) and the serial search strategy of simulated annealing (SA) were involved. Additionally, the learning principle of particle swarm optimization(PSO) and the tabu search strategy were adopted into the SOLA, wherein the integrated frame work was different from traditional optimization methods and the interactive learning strategy was involved in the process of random searching. SOLA was divided into two handling courses: self-learning and interdependent-learning. The local optimal solution would be achieved through self-learning in the process of local searching and the global optimal solution would be achieved via the interdependent learning based on the information sharing mechanism. The search strategies and controlled parameters of SOLA were adaptively fixed according to the feedback information from interactive learning with the environments thus SOLA is self-organizing and intelligent. Experiments for the multidimensional testbed functions showed that SOLA was far superior to traditional optimization methods at the robustness and the global search capability while the solution space ranged from low-dimensional space to the high-dimensional space.Keywordsself-organizinglearning principlehigh-dimensional spacegenetic algorithm

Traffic flow forecasting is the basis for the dynamic control and application of Intelligent Transport Systems (ITS), which is of great practical importance in reducing road congestion. Accurately predicting traffic flow remains a significant obstacle due to the intricate spatial and temporal dependencies associated with traffic movement. To capture the simultaneous dynamics of traffic flow’s spatiotemporal features, this paper suggests a novel Spatiotemporal Interactive Dynamic Synchronous Graph Convolution Network (STIDSG) for traffic flow forecasting, which is composed of the Interactive Dynamic Graph Convolution Network (IDGCN) and the Spatiotemporal Synchronous Graph Convolution Layer (STSGCL). IDGCN learns and shares features using an interactive learning strategy that is based on Dynamic Graph Convolution (DGCN). By incorporating DGCN into the interactive learning structure, it is possible to learn dynamic spatial features of traffic flow in an interactive manner while also capturing temporal dependencies. STSGCL effectively captures the intricate local and dynamic correlations of traffic flows by designing numerous Spatiotemporal Synchronization Graph Convolution Modules (STSGCM) for different time periods. The results of the experiment demonstrate that the STIDSG model suggested in this paper can proficiently extract the changing spatiotemporal features of the traffic flow and outperforms the commonly used baseline methods for prediction. The results of the experiment demonstrate that the STIDSG model suggested in this paper can proficiently extract the changing spatiotemporal features of the traffic flow and outperforms the commonly used baseline methods for prediction.

Some education specialists are challenging the use of traditional strategies in classrooms and are calling for the use of contemporary teaching and learning techniques. In response to these calls, many field experiments that compare different teaching and learning strategies have been conducted. However, to date, little is known on the outcomes of these strategies specific to the context of engineers and engineering education. The debate on traditional versus contemporary strategies continues, with many traditionalists holding firm to the view that contemporary techniques diminish the academic quality and integrity of teaching and learning. This paper adds to the current debate by assessing the outcome of interactive lectures, a contemporary teaching strategy, in the context of engineering education. Specifically, this paper examines the effect of interactive lectures on student academic achievement and the learning environment. This paper supported the quantitative survey responses with self-reported student reflections. The findings contradict the traditionalist view of teaching and learning; student performance was not significantly different when interactive learning strategies were employed. However, students held a more positive view toward the learning environment and felt that interactive learning enhanced their overall learning experiences and spurred interest in the subject area. This paper concluded that interactive lecturing can be an effective approach in teaching business management in engineering education.

Mathematics learning in elementary schools, especially in fractional materials, still faces significant challenges where around 70% of students show a passive attitude and lack of enthusiasm in the learning process. This research aims to develop an edugame-based 'MathQuest' learning media to improve the mathematics skills of elementary school students through an interactive learning approach. The research method uses Research and Development (R&D) with the ADDIE model which includes the Analysis, Design, Development, Implementation, and Evaluation stages. The research was carried out at SDN Samar 1 with 30 subjects of 5th grade students, which were divided into a limited test group (10 students) and a broad test (20 students). The research instruments included expert validation sheets, teacher and student response questionnaires, and learning outcome tests. The results of the study showed that MathQuest learning media met the criteria very valid with a media expert validation score of 92.5% and a material expert validation score of 91.6%. From the practical aspect, the media obtained a score of 94% from teachers and 90% from students, indicating the category of very practical. The effectiveness test through the post test resulted in a score of 87.7% on the limited test and 90% on the broad test, indicating that the media is very effective in improving students' mathematical understanding. MathQuest succeeds in creating more interactive, engaging, and fun learning by combining educational elements and game features that motivate students to learn independently and think critically. This media can be implemented as an innovative solution to support the goals of mathematics learning in elementary schools that are relevant to the development of educational technology.

Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN policies based on human corrective feedback, with a method called Deep COACH (D-COACH). This approach not only takes advantage of the knowledge and insights of human teachers as well as the power of DNNs, but also has no need of a reward function (which sometimes implies the need of external perception for computing rewards). We combine Deep Learning with the COrrective Advice Communicated by Humans (COACH) framework, in which non-expert humans shape policies by correcting the agent’s actions during execution. The D-COACH framework has the potential to solve complex problems without much data or time required. Experimental results validated the efficiency of the framework in three different problems (two simulated, one with a real robot), with state spaces of low and high dimensions, showing the capacity to successfully learn policies for continuous action spaces like in the Car Racing and Cart-Pole problems faster than with DRL.

This study explores the critical role of ubiquitous technology in supporting the implementation of Universal Design for Learning (UDL) in inclusive education. Ubiquitous technologies, including real-time applications, metaverse technologies, and UDL-based learning modules, offer flexible, adaptive methods for presenting information, engaging students, and evaluating learning outcomes tailored to individual needs. Within the UDL framework, these technologies contribute to creating inclusive learning environments, enhancing accessibility for students with special needs. Despite their significant potential, challenges remain in their implementation, such as infrastructure limitations, inadequate teacher training, and the digital divide. To overcome these barriers, strong policy support, collaboration among stakeholders, and investments in educational infrastructure are essential. Additionally, teacher training programs focused on the integration of technology into inclusive education are crucial for ensuring effective adoption. The findings suggest that the integration of ubiquitous technologies can make education more inclusive, providing equitable learning opportunities for all students, particularly those with special needs. Furthermore, the research emphasizes the importance of addressing challenges related to technological accessibility, infrastructure, and teacher readiness to maximize the impact of ubiquitous technologies in fostering inclusive educational practices. This study offers key recommendations, including increased collaboration between governments, educational institutions, and private sectors, as well as the need for further research on overcoming implementation challenges and evaluating the impact of ubiquitous technologies on the learning outcomes of students with special needs.

ABSTRACT: The COVID-19 pandemic and the recent technological advancements have given rise to the use of active, interactive, and innovative learning strategies in Higher Education in India that help in promoting collaborative learning. The aim of introducing flipped classroom strategy using some active learning strategies is to evaluate its effectiveness in use and academic performance, develop managerial, problem solving, creativity, and innovative skills in students, the need of the hour in the present situation where 80% of the engineering graduates are unemployable (aspiring minds report, 2019) and to arouse interest in the management courses which are given little emphasis by engineering students in their undergraduate program and which is affecting their placement because of the backlogs in Management courses. This strategy is used for the Management Science course, which is a common paper for all the engineering branches of III/IV B.Tech students. The results showed that the students were highly engaged in learning; interest was aroused in the subject, and there was drastic progress in academic performance, as well as improved problem-solving, creativity, managerial, and innovative skills.

Socio-Scientific Issues (SSI)-based learning has become increasingly significant in addressing global challenges, particularly for Generation Alpha, who are growing up in the digital era. This study aims to analyze the effectiveness of the SSI-based learning approach within the context of the Sustainable Development Goals (SDGs) at the elementary school level. A qualitative research approach with a case study design was employed, involving observations, interviews, and document analysis of educators and students from several elementary schools. The findings indicate that implementing SSI-based learning contributes to enhancing students' understanding of global issues while simultaneously strengthening critical thinking, problem-solving, and collaboration skills. Educators play a central role in designing interactive and technology-based learning strategies to increase student engagement. However, challenges remain in implementing this approach, including teachers' readiness and limited learning resources. Therefore, integrating SSI-based learning within the SDG framework in elementary education can be an effective strategy for improving students' scientific literacy and social awareness. Further research is needed to develop training strategies for educators and expand the implementation of this method across various educational contexts. In conclusion, integrating the SSI approach based on SDGs into science education in elementary schools has great potential to enhance scientific literacy, social awareness, and 21st-century skills among students.

This research aims to explore Generation Z students’ perceptions of using the Ome TV application as a digital platform to improve their English-speaking skills. In the era of technological advancement, Ome TV offers real-time video interactions that provide authentic language practice opportunities with native speakers and global users. Using a qualitative descriptive approach, data were collected through interviews with selected Gen Z participants aged 18–24 who have actively used Ome TV. The findings show that Ome TV offers several advantages, including increased speaking confidence, enhanced vocabulary, improved fluency, and flexibility in time and location, which aligns with the independent and digital-based learning preferences of Generation Z. In addition, the spontaneous nature of conversations encourages learners to apply vocabulary in contextual situations. However, several challenges were also identified, such as unstable internet connections, exposure to inappropriate content, and difficulty understanding different accents. Despite these challenges, most participants viewed the experience positively and considered it a valuable supplement to traditional language learning methods. This study concludes that Ome TV can serve as an effective alternative media to support speaking practice, especially for tech-savvy learners. The results are expected to contribute to the development of more interactive and safe technology-based language learning strategies that meet the expectations of today’s digital generation.

Recognising and learning Hijaiyyah letters is fundamental for Quranic literacy among deaf and mute students. However, traditional teaching methods lack inclusivity, relying on auditory and verbal instruction, which poses challenges for students with hearing impairments. This study introduces an innovative mobile application and training kit designed to enhance Hijaiyyah letter recognition and learning through a technology-based approach. The application integrates interactive visual aids, sign language animations, gamified learning modules, and real-time feedback to support individualised learning. The training kit includes tactile learning tools, flashcards, and sign language reference materials to reinforce digital learning with hands-on experiences. A mixed-method research design was employed, incorporating pre- and post-assessment tests with a sample of deaf and mute students in special education settings. Findings indicate significant improvement in letter recognition, retention, and engagement among participants using the proposed learning model. Educators also highlighted the effectiveness of integrating visual, interactive, and kinaesthetic learning strategies. This study contributes to special needs education, Islamic pedagogy, and educational technology by providing a scalable model for enhancing Quranic literacy through digital innovation. Future recommendations include AI-driven adaptive learning, real-time sign language translation, and cloud-based accessibility to further improve inclusivity in Islamic education.