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Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
Susu Zhang; Xueying Tang; Qiwei He; Jingchen Liu; Zhiliang Ying – Grantee Submission, 2024
Computerized assessments and interactive simulation tasks are increasingly popular and afford the collection of process data, i.e., an examinee's sequence of actions (e.g., clickstreams, keystrokes) that arises from interactions with each task. Action sequence data contain rich information on the problem-solving process but are in a nonstandard,…
Descriptors: Correlation, Problem Solving, Computer Assisted Testing, Prediction
Guerrero, Tricia A.; Wiley, Jennifer – Grantee Submission, 2019
Teachers may wish to use open-ended learning activities and tests, but they are burdensome to assess compared to forced-choice instruments. At the same time, forced-choice assessments suffer from issues of guessing (when used as tests) and may not encourage valuable behaviors of construction and generation of understanding (when used as learning…
Descriptors: Computer Assisted Testing, Student Evaluation, Introductory Courses, Psychology
Anita B. Delahay; Marsha C. Lovett – Grantee Submission, 2018
Empirically supported multimedia learning (MML) principles [1] suggest effective ways to design instruction, generally for elements on the order of a graphic or an activity. We examined whether the positive impact of MML could be detected in larger instructional units from a MOOC. We coded instructional design (ID) features corresponding to MML…
Descriptors: Multimedia Instruction, Instructional Design, MOOCs, Computer Assisted Testing
Kim, Young-Suk Grace; Vorstius, Christian; Radach, Ralph – Grantee Submission, 2018
The goal of this study was to investigate the nature of online comprehension monitoring, its predictors, and its relation to reading comprehension. Questions were concerned with (a) beginning readers' sensitivity to inconsistencies, (b) predictors of online comprehension monitoring, and (c) the relation of online comprehension monitoring to…
Descriptors: Reading Comprehension, Eye Movements, Listening Comprehension, Reading Processes
Adjei, Seth A.; Botelho, Anthony F.; Heffernan, Neil T. – Grantee Submission, 2016
Prerequisite skill structures have been closely studied in past years leading to many data-intensive methods aimed at refining such structures. While many of these proposed methods have yielded success, defining and refining hierarchies of skill relationships are often difficult tasks. The relationship between skills in a graph could either be…
Descriptors: Prediction, Learning Analytics, Attribution Theory, Prerequisites
Higgs, Karyn; Magliano, Joseph P.; Vidal-Abarca, Eduardo; Martínez, Tomas; McNamara, Danielle S. – Grantee Submission, 2015
Some individual difference factors are more strongly correlated with performance on postreading questions when the text is not available than when it is. The present study explores if similar interactions occur with bridging skill, which refers to a reader's propensity to establish connections between explicit text during reading. Undergraduates…
Descriptors: Correlation, Individual Differences, Undergraduate Students, Reading Processes