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In this study, publications in ERIC about Robotics, Educational Technology in Robotics (ET-in-Robotics), and STEM in Robotics (STEM-in-Robotics) have been reached through data mining. The reached studies were analyzed in terms of their release year, titles, abstracts, and ERIC descriptions. In this process, it aimed to put forward its research trends and features. The analysis of the 1339 publications that were published between 01/01/1973 and 31/12/2021 and available in ERIC has been made by using several Python libraries. These analyses are presented in the form of tables and word clouds. The results showed that the number of publications available in ERIC was the highest between 2017 and 2021. Also, in the last five years, the number of publications available on Robotics in ERIC has gradually increased. In addition, the words "learning", "robotics", and "technology" are important for all three topics whereas the words “child”, "science", “programming” and "teacher" for ET-in-Robotics, and the words "school" and "engineering" for STEM-in-Robotics come to the fore. Moreover, the most frequently assigned descriptor by ERIC staff to these publications has been found to be "teaching methods". When evaluated in general, in the STEM-in-Robotics field, more specific topics were focused, and robotic activities are taken as a type of instructional technology while in the ET-in-Robotics field robotic activities were taken as a type of educational technology. As a result, a publication that will serve as a guide for new research in the field of robotics has been presented. [ABSTRACT FROM AUTHOR]

This exploratory study focuses on understanding the thematic trends and changes in 1,078 articles published in Human Service Organizations: Management, Leadership, and Governance (HSO) journal. Structural Topic Model (STM) was used to examine how topics have changed over time. Fifteen topics were uncovered, and the most widely studied topic in HSO's 40-year history was gender-related issues. The results show that the prevalence of topics varied over time. While some topics (e.g., "Performance and measurement," "Relationship with the market and public sector," "Planning, implementation, and feedback," and "Social work education") showed decreasing trends, others (e.g., "Network and collaboration," "Board and executive," "Program and evidence," and "Supervision and team") showed increasing trends. This study can contribute to reviewing the past social work management research and may help suggest some projections or directions for managers and researchers in human service organizations. [ABSTRACT FROM AUTHOR]

The purpose of this research is to examine the research that has been done on MOOCs by applying data mining and analytic approaches and to depict the current state of MOOC research. The text mining revealed four broad themes: (I) MOOCs as a mainstreaming learning model in HE, (II) motivation and engagement issues in MOOCs, (III) assessment issues in MOOCs, and (IV) MOOCs for social learning. The social network analysis indicated that MOOCs are a significant aspect of online education and that learning analytics are considered a solution to current MOOC handicaps. Both pivotal contribution analysis and timeline analysis demonstrated that MOOC research has a heavy focus on motivation and engagement, high drop out and low retention rates, and instructional quality. Overall, the research concludes that the first wave of MOOC research largely concentrated on the qualitative characteristics of the phenomenon, while the second and third waves of MOOC research concentrated on the quantitative characteristics. [ABSTRACT FROM AUTHOR]

In massive open online courses (MOOCs), learners can interact with each other using discussion boards. Automatically inferring the states or needs of learners from their posts is of interest to instructors, who are faced with a high attrition in MOOCs. Machine learning has previously been successfully used to identify states such as confusion or posting questions, but no solution has yet been provided so that instructors can track the progress of the learners using a validated framework from education research. In this paper, we develop a model to automatically label a post based on the first phase of the interaction analysis model (IAM). This allows instructors to automatically identify whether students are stating opinions, clarifying details, or engaging in activities such as providing examples to peers. Our model is tested on a Coursera MOOC devoted to Chemistry, for which we are able to correctly categorize the IAM status in 4 out of 5 posts. Our approach thus provides instructors with an intelligent system that generates actionable learning assessment data and can cope with large enrollment. Using the system, instructors can quickly identify and remedy learning issues, thus supporting learners in attaining their intended outcomes. [ABSTRACT FROM AUTHOR]

This study explores and characterizes learners' participation patterns in MOOC forums, as well as the factors that correlate with learners' participation. Educational data mining and learning analytics methods were used to retrieve and analyze the learners' interpersonal interaction data, which had accumulated in the Coursera log files. The content in the forums was categorized based on Henri's criteria and converted into quantitative values that could be compared and visualized. It was found that only 20% of the learners were collaborating in the forums throughout the entire course and were responsible for 50% of the total posts. A portion of them earned the name "Super Active." The analyses not only demonstrated the volume of activity and its pattern but also revealed the content of the discussions, which helped to highlight the needs and reasons for students' usage of the forums. The content analysis showed intensity in the "Cognitive" and "Discipline" categories. Thus, forum participants benefit from discussions not only socially but disciplinarily and cognitively as well. Furthermore, even though a strong significant correlation was found between the learners' completion status and their activity in the forums, a group of learners, who did not complete the course, was highly active. [ABSTRACT FROM AUTHOR]

Discussion forums are increasingly central to massive open online courses (MOOCs), and it is vital for learners to participate in associated forumactivities. Active forumparticipation positively relates to learner achievement in that more posts yield better learner performance. However, this numerically aggregated measure overlooks the fact that longitudinal trajectories of forum participation temporally vary among learners with different motivations in taking MOOCs. To provide timely support for learners to stay engaged, it is important to understand the temporal variation of longitudinal forum participation and how different motivations account for the variance. Using educational data mining techniques, this research identified three clusters with different longitudinal participation trajectories and also indicated that intrinsically motivated learners outperformed others in their longitudinal forum engagement. Also, examining longitudinal forum participation more accurately differentiated learner performance than the numerically aggregated measure. Last, learners persistently engaging in forums were more likely to perform better in MOOCs. [ABSTRACT FROM AUTHOR]

Portfolio optimization is always an attractive topic for research. In our previous approach, we proposed a method for mining a group stock portfolio that used grouping genetic algorithms. The derived group stock portfolio represents stocks in the same group that may have similar properties; consequently, a variety of stock portfolios could be offered to investors. However, the evaluation process used by this previous approach is time consuming when the number of stocks or groups increases. To address this problem, the map-reduce technique is considered. Map-reduce is a well-known approach for speeding up the mining process. This paper proposes a map-reduce-based approach to mine groups of stock portfolios and speed up the evolution process while still achieving results as similar as possible to the previous approach. Here, a chromosome represents a mapper number, a group number, a stock part and a portfolio part. Utilizing the mapper number, the chromosomes in a population are divided into subsets and sent to respective mappers, while the reducers execute fitness evaluation and genetic operations. The evolution process is repeated until the terminal conditions are reached. Finally, experiments on a real dataset are conducted to demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Motor-driven equipment (ME) is one of the key components in an air-conditioning system, which contributes to the vast majority of the total energy consumption by air-conditioning systems. Distinguishing variable- and constant-speed equipment is important since the energy simulation models of the two types differ. Traditionally, types of ME are known in advance, and energy consumption data are consequently analyzed. However, in the application scenarios of energy consumption data mining, precedent information on the ME type could be missing. Thus, this study applies this process in reverse, providing new insight into energy consumption data of ME to recognize variable-speed ME in an air-conditioning system. The energy consumption data of ME in an air-conditioning system implemented in a commercial building were collected and numerically analyzed. A proposed simple parameter, coefficient of the median, and several numerical parameters were calculated and used to distinguish variable- from constant-speed ME. Results showed that the energy consumption data distributions of the two types of ME differed. The proposed coefficient of the median could successfully distinguish variable- from constant-speed ME, and it could be applied as an important step in energy consumption data mining of air-conditioning systems. [ABSTRACT FROM AUTHOR]

The temperature of the soil at different depths is one of the most important factors used in different disciplines, such as hydrology, soil science, civil engineering, construction, geotechnology, ecology, meteorology, agriculture, and environmental studies. In addition to physical and spatial variables, meteorological elements are also effective in changing soil temperatures at different depths. The use of machine-learning models is increasing day by day in many complex and nonlinear branches of science. These data-driven models seek solutions to complex and nonlinear problems using data observed in the past. In this research, decision tree (DT), gradient boosted trees (GBT), and hybrid DT–GBT models were used to estimate soil temperature. The soil temperatures at 5, 10, and 20 cm depths were estimated using the daily minimum, maximum, and mean temperature; sunshine intensity and duration, and precipitation data measured between 1993 and 2018 at Divrigi station in Sivas province in Turkey. To predict the soil temperature at different depths, the time windowing technique was used on the input data. According to the results, hybrid DT–GBT, GBT, and DT methods estimated the soil temperature at 5 cm depth the most successfully, respectively. However, the best estimate was obtained with the DT model at soil depths of 10 and 20 cm. According to the results of the research, the accuracy rate of the models has also increased with increasing soil depth. In the prediction of soil temperature, sunshine duration and air temperature were determined as the most important factors and precipitation was the most insignificant meteorological variable. According to the evaluation criteria, such as Nash-Sutcliffe coefficient, R, MAE, RMSE, and Taylor diagrams used, it is recommended that all three (DT, GBT, and hybrid DT–GBT) data-based models can be used for predicting soil temperature. [ABSTRACT FROM AUTHOR]