Martijn Wieling

The impact of online video lecture recordings and automated feedback on student performance

To what extent a blended learning configuration of face-to-face lectures, online on-demand video recordings of the face-to-face lectures and the offering of online quizzes with appropriate feedback has an additional positive impact on the performance of these students compared to the traditional face-to-face course approach? In a between-subjects design in which students were randomly assigned to a group having access to the online lectures including multiple choice quizzes and appropriate feedback or to a group having access to the online lectures only, 474 students (161 men and 313 women) of a course on European Law agreed to participate in the experiment. By using regression analysis we found that the course grade of the students was predicted by their grade point average, their study discipline, their grade goal for the course, the expected difficulty-level of the course, the number of online lectures they viewed, the number of lectures the students attended in person and the interaction between the lectures they viewed online and attended in person. Students who attended few lectures had more benefit from viewing online lectures than students who attended many lectures. In contrast to our expectations, the regression analysis did not show a significant effect of automated feedback on student performance. Offering recordings of face-to-face lectures is an easy extension of a traditional course and is of practical importance, because it enables students who are often absent from the regular face-to-face lectures to be able to improve their course grade by viewing the lectures online.

Quantitative Social Dialectology : Explaining Linguistic Variation Geographically and Socially

In this study we examine linguistic variation and its dependence on both social and geographic factors. We follow dialectometry in applying a quantitative methodology and focusing on dialect distances, and social dialectology in the choice of factors we examine in building a model to predict word pronunciation distances from the standard Dutch language to 424 Dutch dialects. We combine linear mixed-effects regression modeling with generalized additive modeling to predict the pronunciation distance of 559 words. Although geographical position is the dominant predictor, several other factors emerged as significant. The model predicts a greater distance from the standard for smaller communities, for communities with a higher average age, for nouns (as contrasted with verbs and adjectives), for more frequent words, and for words with relatively many vowels. The impact of the demographic variables, however, varied from word to word. For a majority of words, larger, richer and younger communities are moving towards the standard. For a smaller minority of words, larger, richer and younger communities emerge as driving a change away from the standard. Similarly, the strength of the effects of word frequency and word category varied geographically. The peripheral areas of the Netherlands showed a greater distance from the standard for nouns (as opposed to verbs and adjectives) as well as for high-frequency words, compared to the more central areas. Our findings indicate that changes in pronunciation have been spreading (in particular for low-frequency words) from the Hollandic center of economic power to the peripheral areas of the country, meeting resistance that is stronger wherever, for well-documented historical reasons, the political influence of Holland was reduced. Our results are also consistent with the theory of lexical diffusion, in that distances from the Hollandic norm vary systematically and predictably on a word by word basis.

Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features

In this study we use bipartite spectral graph partitioning to simultaneously cluster varieties and identify their most distinctive linguistic features in Dutch dialect data. While clustering geographical varieties with respect to their features, e.g. pronunciation, is not new, the simultaneous identification of the features which give rise to the geographical clustering presents novel opportunities in dialectometry. Earlier methods aggregated sound differences and clustered on the basis of aggregate differences. The determination of the significant features which co-vary with cluster membership was carried out on a post hoc basis. Bipartite spectral graph clustering simultaneously seeks groups of individual features which are strongly associated, even while seeking groups of sites which share subsets of these same features. We show that the application of this method results in clear and sensible geographical groupings and discuss and analyze the importance of the concomitant features.

Evaluating the Pairwise String Alignment of Pronunciations

Pairwise string alignment (PSA) is an important general technique for obtaining a measure of similarity between two strings, used e.g., in dialectology, historical linguistics, transliteration, and in evaluating name distinctiveness. The current study focuses on evaluating different PSA methods at the alignment level instead of via the distances it induces. About 3.5 million pairwise alignments of Bulgarian phonetic dialect data are used to compare four algorithms with a manually corrected gold standard. The algorithms evaluated include three variants of the Levenshtein algorithm as well as the Pair Hidden Markov Model. Our results show that while all algorithms perform very well and align around 95% of all alignments correctly, there are specific qualitative differences in the (mis)alignments of the different algorithms.

Physical predictors of cognitive performance in healthy older adults: a cross-sectional analysis

There is ample evidence that physical and cognitive performance are related, but the results of studies investigating this relationship show great variability. Both physical performance and cognitive performance are constructs consisting of several subdomains, but it is presently unknown if the relationship between physical and cognitive performance depends on subdomain of either construct and whether gender and age moderate this relationship. The aim of this study is to identify the strongest physical predictors of cognitive performance, to determine the specificity of these predictors for various cognitive subdomains, and to examine gender and age as potential moderators of the relationship between physical and cognitive performance in a sample of community-dwelling older adults. In total, 98 men and 122 women (average age 74.0±5.6 years) were subjected to a series of performance-based physical fitness and neuropsychological tests. Muscle strength, balance, functional reach, and walking ability (combined score of walking speed and endurance) were considered to predict cognitive performance across several domains (i.e. memory, verbal attention, visual attention, set-shifting, visuo-motor attention, inhibition and intelligence). Results showed that muscle strength was a significant predictor of cognitive performance for men and women. Walking ability and balance were significant predictors of cognitive performance for men, whereas only walking ability was significant for women. We did not find a moderating effect of age, nor did we find support for a differential effect of the physical predictors across different cognitive subdomains. In summary, our results showed a significant relationship between cognitive and physical performance, with a moderating effect of gender.

Inducing a measure of phonetic similarity from pronunciation variation

Abstract Structuralists famously observed that language is “un systeme ou tout se tient” ( Meillet, 1903 , p. 407), insisting that the system of relations of linguistic units was more important than their concrete content. This study attempts to derive content from relations, in particular phonetic (acoustic) content from the distribution of alternative pronunciations used in different geographical varieties. It proceeds from data documenting language variation, examining six dialect atlases each containing the phonetic transcriptions of the same sets of words at hundreds of different sites. We obtain the sound segment correspondences via an alignment procedure, and then apply an information-theoretic measure, pointwise mutual information, assigning smaller segment distances to sound segment pairs which correspond relatively frequently. We iterate alignment and information-theoretic distance assignment until both remain stable, and we evaluate the quality of the resulting phonetic distances by comparing them to acoustic vowel distances. Wieling, Margaretha, and Nerbonne (2011) evaluated this method on the basis of Dutch and German dialect data, and here we provide more general support for the method by applying it to several other dialect datasets (i.e. Gabon Bantu, U.S. English, Tuscan and Bulgarian). We find relatively strong significant correlations between the induced phonetic distances and the acoustic distances, illustrating the usefulness of the method in deriving valid phonetic distances from distributions of dialectal variation.

Multiple Sequence Alignments in Linguistics

In this study we apply and evaluate an iterative pairwise alignment program for producing multiple sequence alignments, ALPHAMALIG (Alonso et al., 2004), using as material the phonetic transcriptions of words used in Bulgarian dialectological research. To evaluate the quality of the multiple alignment, we propose two new methods based on comparing each column in the obtained alignments with the corresponding column in a set of gold standard alignments. Our results show that the alignments produced by ALPHAMALIG correspond well with the gold standard alignments, making this algorithm suitable for the automatic generation of multiple string alignments. Multiple string alignment is particularly interesting for historical reconstruction based on sound correspondences.

Inducing Sound Segment Differences Using Pair Hidden Markov Models

Pair Hidden Markov Models (PairHMMs) are trained to align the pronunciation transcriptions of a large contemporary collection of Dutch dialect material, the Goeman-Taeldeman-Van Reenen-Project (GTRP, collected 1980--1995). We focus on the question of how to incorporate information about sound segment distances to improve sequence distance measures for use in dialect comparison. PairHMMs induce segment distances via expectation maximisation (EM). Our analysis uses a phonologically comparable subset of 562 items for all 424 localities in the Netherlands. We evaluate the work first via comparison to analyses obtained using the Levenshtein distance on the same dataset and second, by comparing the quality of the induced vowel distances to acoustic differences.

The role of frequency in the retrieval of nouns and verbs in aphasia

ABSTRACT Background: Word retrieval in aphasia involves different levels of processing—lemma retrieval, grammatical encoding, lexeme retrieval, and phonological encoding—before articulation can be programmed and executed. Several grammatical, semantic, lexical, and phonological characteristics, such as word class, age of acquisition, imageability, and word frequency influence the degree of success in word retrieval. It is, however, not yet clear how these factors interact. The current study focuses on the retrieval of nouns and verbs in isolation and in sentence context and evaluates the impact of the mentioned factors on the performance of a group of 54 aphasic speakers. Aims: The main aim is to measure the effect of word frequency on the retrieval of nouns and verb by disentangling the influence of word class, age of acquisition, imageability, and lemma and lexeme frequencies on word retrieval in aphasia. Methods & Procedures: Four tests for retrieval of nouns, verbs in isolation, and infinitives and finite verbs were administered to 54 aphasic speakers. The influence of lemma and lexeme frequency, Age of Acquisition on the word retrieval abilities was analysed. Outcomes and Results: Word class, age of acquisition, and imageability play a significant role in the retrieval of nouns and verbs: nouns are easier than verbs; the earlier a word has been learned and the more concrete it is, the easier it is to retrieve. When performance is controlled for these factors, lemma frequency turns out to play a minor role: only in object naming does it affect word retrieval: the higher the lemma frequency of a noun, the easier it is to access. Such an effect does not exist for verbs, neither on an action-naming test, nor when verbs have to be retrieved in sentence context. Lexeme frequency was not found to be a better predictor than lemma frequency in predicting word retrieval in aphasia. Conclusions: Word retrieval in aphasia is influenced by grammatical, semantic, and lexical factors. Word frequency only plays a minor role: it affects the retrieval of nouns, but not of verbs.

Investigating dialectal differences using articulography

The present study introduces articulography, the measurement of the position of tongue and lips during speech, as a promising method to the study of dialect variation. By using generalized additive modeling to analyze articulatory trajectories, we are able to reliably detect aggregate group differences, while simultaneously taking into account the individual variation across dozens of speakers. Our results on the basis of Dutch dialect data show clear differences between the southern and the northern dialect with respect to tongue position, with a more frontal tongue position in the dialect from Ubbergen (in the southern half of the Netherlands) than in the dialect of Ter Apel (in the northern half of the Netherlands). Thus articulography appears to be a suitable tool to investigate structural differences in pronunciation at the dialect level.