Massive open online courses (MOOCs) are increasingly popular among students of various ages and at universities around the world. The main aim of a MOOC is growth in students’ proficiency. That is why students, professors, and universities are interested in the accurate measurement of growth. Traditional psychometric approaches based on item response theory (IRT) assume that a student’s proficiency is constant over time, and therefore are not well suited for measuring growth. In this study we sought to go beyond this assumption, by (a) proposing to measure two components of growth in proficiency in MOOCs; (b) applying this idea in two dynamic extensions of the most common IRT model, the Rasch model; (c) illustrating these extensions through analyses of logged data from three MOOCs; and (d) checking the quality of the extensions using a cross-validation procedure. We found that proficiency grows both across whole courses and within learning objectives. In addition, our dynamic extensions fit the data better than does the original Rasch model, and both extensions performed well, with an average accuracy of .763 in predicting students’ responses from real MOOCs.
Normative data on the objective age of acquisition (AoA) for 286 Russian words are presented in this article. In addition, correlations between the objective AoA and subjective ratings, name agreement, picture name agreement, imageability, familiarity, word frequency, and word length are provided, as are correlations between the objective AoA and two measures of exemplar dominance (exemplar generation frequency and the number of times an exemplar was named first). The correlations between the aforementioned variables are generally consistent with the correlations reported in other normative studies. The objective AoA data are highly correlated with the subjective AoA ratings, whereas the correlations between the objective AoA and other psycholinguistic variables are moderate. The correlations between the objective AoA of Russian words and similar data for other languages are moderately high. The complete word norms may be downloaded from supplementary material.
The present article introduces a Russian-language database of 375 action pictures and associated verbs with normative data. The pictures were normed for name agreement, conceptual familiarity, and subjective visual complexity, and measures of age of acquisition, imageability, and image agreement were collected for the verbs. Values of objective visual complexity, as well as information about verb frequency, length, argument structure, instrumentality, and name relation, are also provided. Correlations between these parameters are presented, along with a comparative analysis of the Russian name agreement norms and those collected in other languages. The full set of pictorial stimuli and the obtained norms may be freely downloaded from http://neuroling.ru/en/db.htm for use in research and for clinical purposes.
The article introduces the new corpus of eye-movements in silent reading – the Russian Sentence Corpus (RSC). Russian uses Cyrillic script that has not yet been investigated in cross-linguistic eye-movement research. As in every language studied so far, we have confirmed the expected effects of low-level parameters, such as word length, frequency, and predictability, on the eye-movements of skilled Russian readers. These findings allow us to add Slavic languages using Cyrillic script (exemplified by Russian) to the growing number of languages with different orthographies ranging from the Roman-based European languages to logographic Asian ones whose basic eye-movement benchmarks conform to the universal comparative science of reading (Share 2008). We additionally report basic descriptive corpus statistics, and three exploratory investigations of the effects of Russian morphology on the basic eye-movement measures that illustrate the kinds of questions researchers can answer using the RSC. The annotated corpus is freely available from the project page at Open Science Framework: https://osf.io/x5q2r/.
In this article, we present StimulStat – a lexical database for the Russian language in the form of a web appli- cation. The database contains more than 52,000 of the most frequent Russian lemmas and more than 1.7 million word forms derived from them. These lemmas and forms are char- acterized according to more than 70 properties that were dem- onstrated to be relevant for psycholinguistic research, includ- ing frequency, length, phonological and grammatical proper- ties, orthographic and phonological neighborhood frequency and size, grammatical ambiguity, homonymy and polysemy. Some properties were retrieved from various dictionaries and are presented collectively in a searchable form for the first time, the others were computed specifically for the database. The database can be accessed freely at http://stimul. cognitivestudies.ru.