The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion

Crossley, Scott A.; Kyle, Kristopher; McNamara, Danielle S.

doi:10.3758/s13428-015-0651-7

Cited by 214 publications

(119 citation statements)

References 26 publications

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“…On the text side, one can analyze the potential structural key features via descriptive tools such as the 4 × 4 matrix that combines four text levels (metric, phonological, morpho-syntactic, semantic) with four groups of features (sublexical, lexical, interlexical, supralexical; Jacobs, 2015b). Many of these features can be quantified by appropriate tools like the BAWL, Coh-Metrix (Graesser et al, 2004) or TAACO (Crossley et al, 2015), and then fed into regression analyses to find out which features affect which response variables. On the reader side, longer pieces of text like stories and novels should increase the likelihood of triggering personal memories which play a key role for story comprehension (e.g., Larsen and Seilman, 1988; Pleh, 2003; Burke, 2011).…”

Section: Multiword Expressionsmentioning

confidence: 99%

On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question

2016

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Perhaps the most ubiquitous and basic affective decision of daily life is deciding whether we like or dislike something/somebody, or, in terms of psychological emotion theories, whether the object/subject has positive or negative valence. Indeed, people constantly make such liking decisions within a glimpse and, importantly, often without expecting any obvious benefit or knowing the exact reasons for their judgment. In this paper, we review research on such elementary affective decisions (EADs) that entail no direct overt reward with a special focus on Neurocognitive Poetics and discuss methods and models for investigating the neuronal and cognitive-affective bases of EADs to verbal materials with differing degrees of complexity. In line with evolutionary and appraisal theories of (aesthetic) emotions and data from recent neurocognitive studies, the results of a decision tree modeling approach simulating EADs to single words suggest that a main driving force behind EADs is the extent to which such high-dimensional stimuli are associated with the “basic” emotions joy/happiness and disgust.

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Section: Multiword Expressionsmentioning

confidence: 99%

On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question

2016

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“…Transcripts were separated by learner and cleaned to remove all nonlinguistic information including metadata and nonlinguistic vocalizations such as coughs and laughs. Each transcript was run through three NLP tools: the Tool for the Automatic Analysis of Lexical Sophistication (TAALES;Kyle & Crossley, 2015), the Tool for the Automatic Analysis of Cohesion (TAACO;Crossley, Kyle, & McNamara, 2016), and the SEntiment ANalysis and Cognition Engine (SEANCE;Crossley, …”

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confidence: 99%

Learning linkages: Integrating data streams of multiple modalities and timescales

Liu

Stamper

Davenport

et al. 2018

Computer Assisted Learning

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Increasingly, student work is being conducted on computers and online, producing vast amounts of learning‐related data. The educational analytics fields have produced many insights about learning based solely on tutoring systems' automatically logged data, or “log data.” But log data leave out important contextual information about the learning experience. For example, a student working at a computer might be working independently with few outside influences. Alternatively, he or she might be in a lively classroom, with other students around, talking and offering suggestions. Tools that capture these other experiences have potential to augment and complement log data. However, the collection of rich, multimodal data streams and the increased complexity and heterogeneity in the resulting data pose many challenges to researchers. Here, we present two empirical studies that take advantage of multimodal data sources to enrich our understanding of student learning. We leverage and extend quantitative models of student learning to incorporate insights derived jointly from data collected in multiple modalities (log data, video, and high‐fidelity audio) and contexts (individual vs. collaborative classroom learning). We discuss the unique benefits of multimodal data and present methods that take advantage of such benefits while easing the burden on researchers' time and effort.

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“…The findings are divided into three categories: findings involving data drawn from clickstream logs concerning time spent on specific activities within the MOOC (Rules 1-2), findings involving data drawn from the discussion forum that look at the participants' posting behavior (Rules 3-9), and findings involving data from the forum posts that look at linguistic features of the participants' contributions (Rules 10-15). The Tool for the Automated Analysis of Lexical Sophistication 1.4, or TAALES [30], and the Tool for the Automatic Analysis of Cohesion 1.0, or TAACO [31], were used to generate the linguistic variables used in the analyses.…”

Section: Scope Of Analysismentioning

confidence: 99%

Studying MOOC completion at scale using the MOOC replication framework

Andrés

Baker

Gašević

et al. 2018

Proceedings of the 8th International Conference on Learning Analytics and Knowledge

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Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. In the proof of concept presented here, we use MORF to attempt to replicate 15 previously published findings across 29 iterations of 17 MOOCs. The findings indicate that 12 of the 15 findings replicated significantly across the data sets. Results contradicting previously published findings were found in two cases. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.

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The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion

Cited by 214 publications

References 26 publications

On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question

On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question

Learning linkages: Integrating data streams of multiple modalities and timescales

Studying MOOC completion at scale using the MOOC replication framework

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