Applying a Computer-Assisted Tool for Semantic Analysis of Writing: Uses for STEM and ELL

Abstract

In addition to human, close reading of student text with rubrics for assessment, educators use nonhuman, distant computer-assisted tools to help quantitatively measure otherwise qualitative keywords to prevent bias in grading and help read beyond the sentence for underlying cognitions. We apply the Linguistic Inquiry and Word Count (LIWC) software tool to analyze different forms of student writing used in STEM education and research to assess writing of native English speakers and non-native English Language Learners (ELLs), including international students. Available in several languages, LIWC measures four summary variables, Analytical Thinking , Clout , Authentic , and Emotional Tone , to provide outputs as raw word counts, as percentages of words used relative to the text compared with a dictionary of words in categories and sub-dictionaries, and as scores correlating these words algorithmically based on a dictionary of terms associated with underlying meanings. This tool can help measure student personal reflective writing for underlying psychosocial indicators or the cognitive and analytical process in other science writing. By selecting key variables, or creating a personal dictionary, LIWC can be used to analyze scientific writing to detect progressive development of student analytical writing from early draft to final version for different informal and formal writing styles. We share methods, examples, and the potential for using LIWC measures of cognitive processes for different measures of student writing in science courses.

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

“…The Linguistic Inquiry Word Count (LIWC; Pennebaker, Booth, Boyd & Francis, 2015) is a validated tool that searches for target words or word stems from an extensive dictionary, categorizes them into linguistic dimensions and then, converts the raw counts to percentages of total words (Abe, 2020; Khazaei, Lu, & Mercer, 2017; Simms et al ., 2017; Smith‐Keiling & Hyun, 2019). LIWC uses predetermined categories and will analyze and classify words within these preassigned categories (Pilny et al ., 2019).…”

“…Clout has also been described as an indication of expertise in professional settings (Newman et al ., 2016), a barometer for relative social status or leadership (O’Dea, Larsen, Batterham, Calear, & Christensen, 2017) and even a bellwether for bias (Hagiwara, Slatcher, Eggly, & Penner, 2017). The role of clout has been examined in a variety of contexts including education (Moore et al ., 2019; Oliver et al ., 2020; Smith‐Keiling & Hyun, 2019; Smith‐Keiling, Swanson, & Dehnbostel, 2018), mental health (O’Dea et al ., 2017; Tadesse, Lin, Xu, & Yang, 2019), dreams (Bulkeley & Graves, 2018), social hierarchies (Kacewicz et al ., 2014) and bias (Hagiwara et al ., 2017).…”

“…However, these forums also present challenges due to the amount of data that is generated. Being able to efficiently mine this data for context, understanding and patterns is a challenge that can be addressed through the use of text mining, specifically automated text analysis techniques and tools (Dowell, Graesser, & Cai, 2016;Gardner & Brooks, 2018;Krippendorff, ) is a validated tool that searches for target words or word stems from an extensive dictionary, categorizes them into linguistic dimensions and then, converts the raw counts to percentages of total words (Abe, 2020;Khazaei, Lu, & Mercer, 2017;Simms et al, 2017;Smith-Keiling & Hyun, 2019). LIWC uses predetermined categories and will analyze and classify words within these preassigned categories (Pilny et al, 2019).…”

Psycholinguistic Markers of Therapeutic Rupture Types

“…The program is also composed of 90 analyzable output variables, which focus mainly on psychological processes. The variables are scored by comparing the percentage of words being analyzed to a dictionary of words in categories and subdictionaries (Smith-Keiling & Hyun, 2019).…”

A Systematic Approach to Promote Environmental Engineering Students’ Learning in Environmental Molecular Microbiology

“…Additionally, there are many teaching methods that have been proven to promote students’ learning and critical thinking, thereby improving student performance. These methods include the case study teaching method ( 7 ), computer-assisted tools ( 8 ), preassessment and in-class questions ( 9 ), and the argument-and-plan method ( 10 ). For example, case studies have been identified as being more effective than classroom discussions and textbook reading in promoting learning of biological concepts and development of written and oral communication skills ( 7 ).…”