Search Results (9)

In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting. Full article

Computational thinking (CT) and creativity have been recognized as crucial skills for adapting to the current digital era. However, despite being extensively studied over the last few decades, research on their associations has only emerged recently. We report on a study that examined how creativity is manifested in the context of CT, specifically while solving computational problems in an online game-based learning environment for early programming. We took a learning analytics log-based approach to evaluate measures of CT and creativity. We developed a Python algorithm to automatically analyze the logged solutions across four creativity measures. This allowed for an objective, quantitative, multidimensional analysis of 52,438 submissions of N = 111 primary and secondary school students over 85 tasks. We examined the relationships between measures of creativity, game level, and CT, utilized exploratory analysis to investigate how measures of creativity differ across age groups, and explored how these measures characterize students. Our findings suggest that creativity does not decrease throughout the game despite the increased difficulty and its mechanics that penalize creative solutions. We also point out how various dimensions of creativity play different roles in learning. These findings suggest that educators should foster intrinsic motivation and encourage students to explore multiple solution paths to enhance both CT and creativity skills. Researchers should keep investigating mechanisms to assess and enhance creativity in learning environments and explore the influence of personal and contextual factors. Full article

Many regular business operations are transforming into digital services, increasing advanced multi-platforms, rapid operational alignment, flexibility, and environmental impact through energy consumption, hardware waste, and technology investments. Flexible and sustainable system development models emphasizing energy efficiency can help innovate software development as digital servicing applications shift. This research is motivated by the need to improve energy consumption in early software design and development due to rising technological efficiency and sustainability demands. Although effective in iterative development and stakeholder engagement, traditional Agile methodologies often struggle with long-term sustainability and energy efficiency. Extended Agile, combining Agile, layered architecture, and aspect-oriented frameworks (ALAI), promises to improve system modularity, flexibility, maintainability, and sustainability. This study’s findings are not just theoretical, but also practically relevant, as they explore the energy efficiency of ALAI software development methodologies, using graduate admission information system services (GAISS) as an example. GAISS is a complex system that handles the entire process of graduate admissions, from application submission to final decision. The study quantifies the energy usage of a student-list webpage by analyzing Microsoft IIS server logs from February 2022 to May 2024. Directly applicable findings show that the GAISS based on the ALAI framework reduces energy consumption by 10.7914% compared to traditional Agile software developments. ALAI used 892.80 kWh versus Agile’s 1000.80 kWh during operations, saving energy. These findings demonstrate the benefits of integrating aspect-oriented frameworks and layering approaches into Agile methodologies, contributing to sustainable software development discourse. The study emphasizes the importance of energy-efficient frameworks such as ALAI to reduce software systems’ environmental impact and promote software development sustainability. The findings of this study, with their practical relevance, assist software developers and organizations in choosing software design and development methods that maximize operational efficiency and environmental sustainability. Full article

Learning mostly involves communication and interaction that leads to new information being processed, which eventually turns into knowledge. In the digital era, these actions pass through online technologies. Formal education uses LMSs that support these actions and, at the same time, produce massive amounts of data. In a distance learning model, the assignments have an important role besides assessing the learning outcome; they also help students become actively engaged with the course and regulate their learning behavior. In this work, we leverage data retrieved from students’ online interactions to improve our understanding of the learning process. Focusing on log data, we investigate the students’ activity that occur close to and during assignment submission due dates. Additionally, their activity in relation to their academic achievements is examined and the response time in the forum communication is computed both for students and their tutors. The main findings include that students tend to procrastinate in the submission of their assignments mostly at the beginning of the course. Furthermore, the last-minute submissions are usually made late at night, which probably indicates poor management or lack of available time. Regarding forum interactions, our findings highlight that tutors tend to respond faster than students in the corresponding posts. Full article

Born-digital content is rapidly becoming the norm for literary works, professional reports, academic journal articles, and formal corporate correspondence. From the perspective of digital forensics, there is a need to understand the origin of a document and its entire creation process, from outlining and drafting to editing the final version of the text. Revision save identifier (RSID) numbers embedded in MS Word documents have been used to examine the nature and extent of individual edits within a document. These RSIDs remain logged in the metadata even if the text with which they were associated has been removed. As copies of such files retain the original’s RSIDs, this metadata can be used to determine the order in which documents were cloned from each other. As a proof-of-concept, this paper examined over 400 template files generated by a single publisher for manuscript submissions to its journals. The study can show that it is possible to establish genealogies and thus relative chronologies of born digital content by first identifying those documents that share a document (root) RSID and then seriating those RSIDs that are shared between two or more documents. Full article

In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department. Full article

A timber market occupies a very particular position within the economic reality. Trading of commodities such as precious timber is, indeed, strongly conditioned by the carrying capacity and the silvicultural potential of the forest ecosystem. Timber markets in Poland are characterized by a controlling position of the State Forests, and one of the possible forms of wood sale is the system of submission. A submission usually implies that small quantities of wood with unusual features are being offered to a specific group of customers. The paper presents the sale results and prices of veneer wood commercialized in submission systems and in other forms of timber sale in the territory of Krosno during the years 2000−2019. It is one of the oldest submission markets in Poland, where the most expensive log ever in Poland was sold (13,000 USD/log—close to 7000 USD/m³). The Regional Directorate of State Forest (RDSF) of Krosno is located in the south-eastern part of Poland and manages a forest area of approximately 400,000 hectares. Annual timber harvesting amounts to 2 million m³, of which less than 2000 m³ annually is allocated to the submissions. The data cover a 20-year continuous time series and allow tracing changes in the wood volume offered to the market, the species population structure, and price trends for individual species. The data are being discussed against the background of the economic situation and in relation to the average prices obtained from other methods of sale. Beech was the most sold, but the demands for oak and sycamore appeared to be particularly high during the period of observation. The unity prices can be very variable even for wood from the same species, especially for sycamore. The prices are generally demand-driven and show strong influences from furniture industries and fashion. A rising demand for high quality timber and logs of big dimensions has been noticed. The submission system results in substantial economic benefits for the forest management and the region as a whole. Full article

High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India. Plantix, a free app that uses image recognition to help farmers diagnose crop diseases, logged 9 million geolocated photos from 2017–2019 in India, 2 million of which are in the states of Andhra Pradesh and Telangana in India. Crop type labels based on farmer-submitted images were added by domain experts and deep CNNs. The resulting dataset of crop type at coordinates is high in volume, but also high in noise due to location inaccuracies, submissions from out-of-field, and labeling errors. We employed a number of steps to clean the dataset, which included training a CNN on very high resolution DigitalGlobe imagery to filter for points that are within a crop field. With this cleaned dataset, we extracted Sentinel time series at each point and trained another CNN to predict the crop type at each pixel. When evaluated on the highest quality subset of crowdsourced data, the CNN distinguishes rice, cotton, and “other” crops with 74% accuracy in a 3-way classification and outperforms a random forest trained on harmonic regression features. Furthermore, model performance remains stable when low quality points are introduced into the training set. Our results illustrate the potential of non-traditional, high-volume/high-noise datasets for crop type mapping, some improvements that neural networks can achieve over random forests, and the robustness of such methods against moderate levels of training set noise. Lastly, we caution that obstacles like the lack of good Sentinel-2 cloud mask, imperfect mobile device location accuracy, and preservation of privacy while improving data access will need to be addressed before crowdsourcing can widely and reliably be used to map crops in smallholder systems. Full article

Computer basic teaching is an essential basic learning content in higher education teaching. In order to encourage students and enable them to practice and improve their programming ability, the online judge system has been introduced into the programming course for compiling, executing and evaluating the algorithm source code submitted by students. The asymmetry of students’ programming level is an important issue when teachers guide the programming of online judge system. We used the exploratory factor analysis method to identify the potential variable structure from the log data submitted by the students of the online judge system, and evaluate the programming level of the students to predict the “at risk” learners. We proposed a student participation model, SCFH, based on this variable structure. Using the log data of the students in the C language course and their final exam results, we trained a deep neural network based on SCFH to divide the students into three different grades, namely “risky”, “intermediate” and “advanced”. To verify the validity of the model, we used the prediction model to classify students in another C++ language programming course. The results show that the submission log data model SCFH can be used to predict the programming ability of students, and the validity of these results can be tested by examination results. Full article