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The Uses Of Argument

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Клініко-епідеміологічні особливості спалаху кору в м. Суми у 2014 році

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Automated Evaluation of Text and Discourse with Coh-Metrix: Introduction

Encyclopedia of the Sciences of Learning

This chapter addresses students’ data interpretation, a key NGSS inquiry practice, with which students have several different types of difficulties. In this work, we unpack the difficulties associated with data interpretation from those associated with warranting claims. We do this within the context of Inq-ITS (Inquiry Intelligent Tutoring System), a lightweight LMS, providing computer-based assessment and tutoring for science inquiry practices/skills. We conducted a systematic analysis of a subset of our data to address whether our scaffolding is supporting students in the acquisition and transfer of these inquiry skills. We also describe an additional study, which used Bayesian Knowledge Tracing (Corbett and Anderson. User Model User-Adapt Interact 4(4):253–278, 1995), a computational approach allowing for the analysis of the fine-grained sub-skills underlying our practices of data interpretation and warranting claims.

Real-Time Scaffolding of Students’ Online Data Interpretation During Inquiry with Inq-ITS Using Educational Data Mining

Researchers are trying to develop assessments for inquiry practices to elicit students’ deep science learning, but few studies have examined the relationship between students’ doing, i.e. performance assessment, and writing, i.e. open responses, during inquiry. Inquiry practices include generating hypotheses, collecting data, interpreting data, warranting claims, and communicating findings [1]. The first four practices involve “doing” science, whereas the last involves writing scientific explanations, i.e. arguing using evidence. In this study, we explored whether what students wrote in their constructed responses reflected what they did during science inquiry in the Inq-ITS system. Results showed that more than half of the students’ writing did not match what they did in the environment. Findings revealed multiple types of students in the messy middle, which has implications for both teacher instruction and intelligent tutoring systems, such as Inq-ITS, in terms of providing real-time feedback for students to address the full complement of inquiry practices [1].

Dusting Off the Messy Middle: Assessing Students’ Inquiry Skills Through Doing and Writing

Scientific explanations, which include a claim, evidence, and reasoning (CER), are frequently used to measure students’ deep conceptual understandings of science. In this study, we developed an automated scoring approach for the CER that students constructed as a part of virtual inquiry (e.g., formulating questions, analyzing data, and warranting claims) in an intelligent tutoring system (ITS), called Inq-ITS. Results showed that the automated scoring of CER was strongly correlated with human scores when validated using independent sets of data from both the same inquiry task/question, as well as when using data from a different inquiry task/question. These findings imply that automated CER is a very promising approach to reliably and efficiently score scientific explanations in open response format for both smalland largescale assessments. It also provides Inq-ITS with the capability to assess the full complement of inquiry practices described by NGSS.

http://educationaldatamining.org/EDM2017/proc_files/papers/paper_55.pdf

Automated Assessment for Scientific Explanations in On-Line Science Inquiry.

Computer-assisted assessment environments, such as intelligent tutoring systems, simulations, and virtual environments are now being designed to measure students’ science inquiry practices. Some assessment environments not only evaluate students’ inquiry practice competencies, but also provide real-time scaffolding in order to help students learn. The present study aims to examine the impact of real-time scaffolding from an animated, pedagogical agent on students’ inquiry performance across a number of practices. Participants were randomly assigned to one of two conditions: receiving scaffolding or no scaffolding. All participants completed three virtual labs: Flower (a general pretest), Phase Change, and Density. Results showed that students who received immediate feedback during assessment performed better on subsequent inquiry tasks. These findings have implications for designers and researchers regarding the benefits of including real-time scaffolding within intelligent assessment systems.

The Impact of Multiple Real-Time Scaffolding Experiences on Science Inquiry Practices

The Next Generation Science Standards (NGSS) for K-12 science education has outlined new standards that integrate science inquiry practices with scientific concepts and ideas. The challenge with im...

Rachel Dickler

Papers

Real-Time Scaffolding of Students’ Online Data Interpretation During Inquiry with Inq-ITS Using Educational Data Mining

Dusting Off the Messy Middle: Assessing Students’ Inquiry Skills Through Doing and Writing

Automated Assessment for Scientific Explanations in On-Line Science Inquiry.

The Impact of Multiple Real-Time Scaffolding Experiences on Science Inquiry Practices

Advanced Educational Technology for Science Inquiry Assessment