scholarly journals Predicting essay quality from search and writing behavior

2021 ◽  
Author(s):  
Pertti Vakkari ◽  
Michael Völske ◽  
Martin Potthast ◽  
Matthias Hagen ◽  
Benno Stein
Keyword(s):  
Essay Quality

Are Individuals Sensitive to Changes in Performance when Interrupted?

2016 ◽  
Vol 60 (1) ◽  
pp. 241-245
Author(s):  
Cyrus K. Foroughi ◽  
Nicole E. Werner ◽  
Deborah A. Boehm-Davis
Keyword(s):  
Likert Scale ◽  
Subjective Ratings ◽  
Actual Performance ◽  
Essay Quality

Although there is an abundance of research showing how interruptions disrupt performance, there is little evidence regarding whether individuals believe they are personally affected by interruptions. The goal for this research was to determine whether individuals are sensitive to changes in performance when interrupted. Thirty-two participants wrote essays while interrupted or not interrupted and rated the quality of their essay on a 7-point Likert scale. Afterwards, the participants answered a separate 7-point Likert scale question that directly asked whether they believed that the interruptions negatively affected their performance. Participants' subjective ratings of their essay quality were not affected by the presence or absence of an interruption even though actual essay quality suffered from the interruptions. Additionally, a majority of the participants claimed that the interruptions had no or little impact on performance. Our data suggest individuals may not be sensitive to differences in their actual performance when interrupted.

scholarly journals Learning to Give Feedback: Modeling Attributes Affecting Argument Persuasiveness in Student Essays

2018 ◽  
Author(s):  
Zixuan Ke ◽  
Winston Carlile ◽  
Nishant Gurrapadi ◽  
Vincent Ng
Keyword(s):  
Neural Models ◽  
Automated Essay Scoring ◽  
Essay Scoring ◽  
Essay Quality

Argument persuasiveness is one of the most important dimensions of argumentative essay quality, yet it is little studied in automated essay scoring research. Using a recently released corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness, we design and train the first set of neural models that predict the persuasiveness of an argument and its attributes in a student essay, enabling useful feedback to be provided to students on why their arguments are (un)persuasive in addition to how persuasive they are.

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