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Marzouk, Zahia; Rakovic, Mladen; Liaqat, Amna; Vytasek, Jovita; Samadi, Donya; Stewart-Alonso, Jason; Ram, Ilana; Woloshen, Sonya; Winne, Philip H.; Nesbit, John C. – Australasian Journal of Educational Technology, 2016
Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students' decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning…
Descriptors: Science Education, Science Instruction, Data Collection, Cooperation
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Ye, Li; Oueini, Razanne; Dickerson, Austin P.; Lewis, Scott E. – Chemistry Education Research and Practice, 2015
This study used a series of text message inquiries sent to General Chemistry students asking: "Have you studied for General Chemistry I in the past 48 hours? If so, how did you study?" This method for collecting data is novel to chemistry education research so the first research goals were to investigate the feasibility of the technique…
Descriptors: Computer Mediated Communication, Telecommunications, Science Instruction, Chemistry
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior
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Bellocchi, Alberto; King, Donna T.; Ritchie, Stephen M. – International Journal of Science Education, 2016
There is on-going international interest in the relationships between assessment instruments, students' understanding of science concepts and context-based curriculum approaches. This study extends earlier research showing that students can develop connections between contexts and concepts--called "fluid transitions"--when studying…
Descriptors: Comparative Analysis, Student Reaction, Chemistry, Science Education
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Hakverdi-Can, Meral; Dana, Thomas M. – Turkish Online Journal of Educational Technology - TOJET, 2012
The purpose of this study is to examine exemplary science teachers' level of computer use, their knowledge/skills in using specific computer applications for science instruction, their use of computer-related applications/tools during their instruction, how often they required their students to use those applications in or for their science class…
Descriptors: Computer Uses in Education, Computers, Educational Technology, Science Teachers
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Hovick, James W.; Murphy, Michael; Poler, J. C. – Journal of Chemical Education, 2007
The study describes the development and advantages of various correlation techniques that are used for data extraction and are integral to all modern instrumentation. The "Audibilization" of the electronic signals in such cases is found to be very essential for the technique.
Descriptors: Chemistry, Science Instruction, Science Laboratories, Correlation
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Snyder, Robert – Science Scope, 2005
Collecting weather data is a traditional part of a meteorology unit at the middle level. However, making connections between the data and weather conditions can be a challenge. One way to make these connections clearer is to enter the data into a database. This allows students to quickly compare different fields of data and recognize which…
Descriptors: Meteorology, Databases, Weather, Data Collection
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection