Technological tools for science classrooms : choosing and using for productive and sustainable teaching and learning experiences

In this age of rapidly evolving technology, teachers are encouraged to adopt ICTs by government, syllabus, school management, and parents. Indeed, it is an expectation that teachers will incorporate technologies into their classroom teaching practices to enhance the learning experiences and outcomes of their students. In particular, regarding the science classroom, a subject that traditionally incorporates hands-on experiments and practicals, the integration of modern technologies should be a major feature. Although myriad studies report on technologies that enhance students’ learning outcomes in science, there is a dearth of literature on how teachers go about selecting technologies for use in the science classroom. Teachers can feel ill prepared to assess the range of available choices and might feel pressured and somewhat overwhelmed by the avalanche of new developments thrust before them in marketing literature and teaching journals. The consequences of making bad decisions are costly in terms of money, time and teacher confidence. Additionally, no research to date has identified what technologies science teachers use on a regular basis, and whether some purchased technologies have proven to be too problematic, preventing their sustained use and possible wider adoption. The primary aim of this study was to provide research-based guidance to teachers to aid their decision-making in choosing technologies for the science classroom. The study unfolded in several phases. The first phase of the project involved survey and interview data from teachers in relation to the technologies they currently use in their science classrooms and the frequency of their use. These data were coded and analysed using Grounded Theory of Corbin and Strauss, and resulted in the development of a PETTaL model that captured the salient factors of the data. This model incorporated usability theory from the Human Computer Interaction literature, and education theory and models such as Mishra and Koehler’s (2006) TPACK model, where the grounded data indicated these issues. The PETTaL model identifies Power (school management, syllabus etc.), Environment (classroom / learning setting), Teacher (personal characteristics, experience, epistemology), Technology (usability, versatility etc.,) and Learners (academic ability, diversity, behaviour etc.,) as fields that can impact the use of technology in science classrooms. The PETTaL model was used to create a Predictive Evaluation Tool (PET): a tool designed to assist teachers in choosing technologies, particularly for science teaching and learning. The evolution of the PET was cyclical (employing agile development methodology), involving repeated testing with in-service and pre-service teachers at each iteration, and incorporating their comments i ii in subsequent versions. Once no new suggestions were forthcoming, the PET was tested with eight in-service teachers, and the results showed that the PET outcomes obtained by (experienced) teachers concurred with their instinctive evaluations. They felt the PET would be a valuable tool when considering new technology, and it would be particularly useful as a means of communicating perceived value between colleagues and between budget holders and requestors during the acquisition process. It is hoped that the PET could make the tacit knowledge acquired by experienced teachers about technology use in classrooms explicit to novice teachers. Additionally, the PET could be used as a research tool to discover a teachers’ professional development needs. Therefore, the outcomes of this study can aid a teacher in the process of selecting educationally productive and sustainable new technology for their science classrooms. This study has produced an instrument for assisting teachers in the decision-making process associated with the use of new technologies for the science classroom. The instrument is generic in that it can be applied to all subject areas. Further, this study has produced a powerful model that extends the TPACK model, which is currently extensively employed to assess teachers’ use of technology in the classroom. The PETTaL model grounded in data from this study, responds to the calls in the literature for TPACK’s further development. As a theoretical model, PETTaL has the potential to serve as a framework for the development of a teacher’s reflective practice (either self evaluation or critical evaluation of observed teaching practices). Additionally, PETTaL has the potential for aiding the formulation of a teacher’s personal professional development plan. It will be the basis for further studies in this field.

Repeated sequences as genetic markers in pooled tissue samples

We show, using the PDR1 element of pea, that dispersed repeated sequences of moderate copy number can be used simply and efficiently to generate markers linked to a trait of interest. Inspection of hybridization patterns of repeated sequences to DNA mixtures of pooled genotypes is a sensitive way of detecting such markers. The large number of bands in tracks of digests of these mixtures allows the simultaneous sampling of loci at many places in the genome, and the many unlinked loci serve as internal controls. It is also shown that intensity ratios calculated from these band differences can be used to give a rough estimate of linkage distance.

A copia-like element in Pisum demonstrates the uses of dispersed repeated sequences in genetic analysis

A DNA sequence between two legumin genes in Pisum is a member of the copia-like class of retrotransposons and represents one member of a polymorphic and heterogeneous dispersed repeated sequence family in Pisum. This sequence can be exploited in genetic studies either by RFLP analysis where several markers can be scored together, or the segregation of individual elements can be followed after PCR amplification of specific members.

A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.

The effect of repeated loading and freeze-thaw cycling on immature bovine thoracic motion segment stiffness

There is growing interest in the biomechanics of ‘fusionless’ implant constructs used for deformity correction in the thoracic spine, however, there are questions over the comparability of in vitro biomechanical studies from different research groups due to the various methods used for specimen preparation, testing and data collection. The aim of this study was to identify the effect of two key factors on the stiffness of immature bovine thoracic spine motion segments: (i) repeated cyclic loading and (ii) multiple freeze-thaw cycles, to aid in the planning and interpretation of in vitro studies. Two groups of thoracic spine motion segments from 6-8 week old calves were tested in flexion/extension, right/left lateral bending, and right/left axial rotation under moment control. Group (A) were tested with continuous repeated cyclic loading for 500 cycles with data recorded at cycles 3, 5, 10, 25, 50, 100, 200, 300, 400 and 500. Group (B) were tested after each of five freeze-thaw sequences, with data collected from the 10th load cycle in each sequence. Group A: Flexion/extension stiffness reduced significantly over the 500 load cycles (-22%; P=0.001), but there was no significant change between the 5th and 200th load cycles. Lateral bending stiffness decreased significantly (-18%; P=0.009) over the 500 load cycles, but there was no significant change in axial rotation stiffness (P=0.137). Group B: There was no significant difference between mean stiffness over the five freeze-thaw sequences in flexion/extension (P=0.813) and a near significant reduction in mean stiffness in axial rotation (-6%; P=0.07). However, there was a statistically significant increase in stiffness in lateral bending (+30%; P=0.007). Comparison of in vitro testing results for immature thoracic bovine spine segments between studies can be performed with up to 200 load cycles without significant changes in stiffness. However, when testing protocols require greater than 200 cycles, or when repeated freeze-thaw cycles are involved, it is important to account for the effect of cumulative load and freeze-thaw cycles on spine segment stiffness.

Classification of echolocation calls from 14 species of bat by Support Vector Machines and Ensembles of Neural Networks

Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance.

The presence of ultrasonic harmonics in the calls of the rifleman (Acanthisitta chloris)

Ultrasonic vocalisations (frequencies > 20 kHz) have been extensively studied in the context of echolocation by bats and other mammals (Sales & Pye 1974; Wilson & Hare 2004). Ultrasonic calls have also been recorded from birds, including the blue-throated hummingbird ( Lampornis clemenciae ) (Pytte et al. 2004), where it was first thought that individuals made use of high pitch calls to avoid masking by background noise in a visually obscured environment. Similarly, city-dwelling great tits ( Parus major ) use song with a higher minimum frequency (although not ultrasonic) compared to woodland birds to communicate with conspecifics to avoid the predominantly low-frequency background noise in the city (Slabbekorn & Peet 2003). The theory that birds use ultrasound to avoid noise masking was discarded when it was discovered that there was no corresponding auditory brainstem response (i.e. sensory perception) to the ultrasonic calls in the hummingbirds producing those calls.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Wing morphology, echolocation calls, diet and emergence time of black-bearded tomb bats (Taphozous melanopogon, Emballonuridae) from southwest China

We studied the wing morphology, echolocation calls, diet and emergence time of the black-bearded tomb bat (Taphozous melanopogon) from May to October 2006 in Guangxi Province, southwest China. Taphozous melanopogon has wings with high aspect ratio, high loading and pointed wing-tip shape-characteristics associated with fast flight in open space. This species usually produces low-intensity, low frequency, and frequency-modulated (FM) calls usually containing up to four harmonics, with most energy in the second (or sometimes third) harmonic. The diet of this species consists mostly of Lepidoptera and Hemiptera. Timing of evening emergence is correlated with the time of sunset. This is the first study to describe the flight and echolocation behavior of this species in China, and opens the way for future studies of its biology.

Echolocation calls, wing shape, diet and phylogenetic diagnosis of the endemic Chinese bat Myotis pequinius

We describe the echolocation calls, flight morphology and diet of the endemic Chinese bat Myotis pequinius Thomas, 1908. Orientation calls are broadband, and reach low terminal frequencies. Diet comprised 80% beetles by volume. Wing shape and call design suggest that the bats fly in cluttered habitats, and the possession of moderately long ears and the dietary composition imply they forage at least sometimes by gleaning. Myotis pequinius resembles a larger Oriental version of the western Palaearctic species M. nattereri. Phylogenetic analysis based on sequences of the cytochrome b gene of mitochondrial DNA (1,140 base pairs) from a range of Palaearctic Myotis species confirmed that M. pequinius is close to the nattereri group, and is a sister-species to the eastern Palaearctic M. bombinus. One bat sequenced from China could not be identified from available species descriptions. It was smaller than M. pequinius, and also differed from it in sequence divergence by 6.7%, suggesting the existence of additional, cryptic taxonomic diversity in this group. Our phylogenetic analysis also supports the recognition of M. schaubi as a species distinct from M. nattereri in Transcaucasia and south-western Asia. Myotis nattereri tschuliensis is more closely related to M. schaubi than to M. nattereri, and is best considered either as a subspecies of M. schaubi, or possibly as a distinct species.

Echolocation calls and wing morphology of bats from the West Indies

Echolocation calls of 119 bats belonging to 12 species in three families from Antillean islands of Puerto Rico, Dominica, and St. Vincent were recorded by using time-expansion methods. Spectrograms of calls and descriptive statistics of five temporal and frequency variables measured from calls are presented. The echolocation calls of many of these species, particularly those in the family Phyllostomidae, have not been described previously. The wing morphology of each taxon is described and related to the structure of its echolocation calls and its foraging ecology. Of slow aerial-hawking insectivores, the Mormoopidae and Natalidae Mormoops blainvillii, Pteronotus davyi davyi, P. quadridens fuliginosus, and Natalus stramineus stramineus can forage with great manoeuvrability in background-cluttered space (close to vegetation), and are able to hover. Pteronotus parnellii portoricensis is able to fly and echolocate in highly-cluttered space (dense vegetation). Among frugivores, nectarivores and omnivores in the family Phyllostomidae, Brachyphylla cavernarum intermedia is adapted to foraging in the edges of vegetation in background-cluttered space, while Erophylla bombifrons bombifrons, Glossophaga longirostris rostrata, Artibeus jamaicensis jamaicensis, A. jamaicensis schwartzi and Stenoderma rufum darioi are adapted to foraging under canopies in highly-cluttered space and do not have speed or efficiency in commuting flight. In contrast, Monophyllus plethodon luciae, Sturnira lilium angeli and S. lilium paulsoni are adapted to fly in highly-cluttered space, but can also fly fast and efficiently in open areas.

Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks.

Time-expanded and heterodyned echolocation calls of the New Zealand long-tailed Chalinolobus tuberculatus and lesser short-tailed bat Mystacina tuberculata were recorded and digitally analysed. Temporal and spectral parameters were measured from time-expanded calls and power spectra generated for both time-expanded and heterodyned calls. Artificial neural networks were trained to classify the calls of both species using temporal and spectral parameters and power spectra as input data. Networks were then tested using data not previously seen. Calls could be unambiguously identified using parameters and power spectra from time-expanded calls. A neural network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 40 kHz (the frequency with the most energy of the fundamental of C. tuberculatus call), could identify 99% and 84% of calls of C. tuberculatus and M. tuberculata, respectively. A second network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 27 kHz (the frequency with the most energy of the fundamental of M. tuberculata call), could identify 34% and 100% of calls of C. tuberculatus and M. tuberculata, respectively. This study represents the first use of neural networks for the identification of bats from their echolocation calls. It is also the first study to use power spectra of time-expanded and heterodyned calls for identification of chiropteran species. The ability of neural networks to identify bats from their echolocation calls is discussed, as is the ecology of both species in relation to the design of their echolocation calls.

The effect of recording situation on the echolocation calls of the New Zealand Lesser short-tailed bat (Mystacina tuberculata Gray).

Using a broad‐band recording system (150 Hz‐100 kHz) the echolocation calls of the lesser short‐tailed bat (Mystacina tuberculata) were recorded under three very different situations: free‐flying, flying within a flight cage, and on release from the hand. Calls of bats landing and feeding on a platform in Wellington Zoo were also recorded. Both the lowest frequency and frequency of peak amplitude of calls were significantly affected by the situation under which calls were recorded. Although the calls of free‐flying bats are different from those produced by bats foraging on the ground, it is unlikely that M. tuberculata uses echolocation to locate prey on the ground. No significant differences could be found between the calls emitted by male and female bats, and no consistent relationships were obvious between temporal and spectral call characteristics. There was some evidence to suggest that individual bats could be identified by their echolocation calls.

Echolocation calls of the long tailed bat: a quantitative analysis of types of calls

The echolocation calls of long-tailed bats (Chalinolobus tuberculatus) were recorded in the Eglinton Valley, Fjordland, New Zealand, and digitized for analysis with the signal-processing software. Univariate and multivariate analyses of measure features facilitated a quantitative classification of the calls. Cluster analysis was used to categorize calls into two groups equating to search and terminal buzz calls described qualitatively for other species. When moving from search to terminal phases, the calls decrease in bandwidth, maximum and minimum frequency of call, and duration. Search calls begin with a steep-downward FM sweep followed by a short, less-modulated component. Buzz calls are FM sweeps. Although not found quantitatively, a broad pre-buzz group of calls also was identified. Ambiguity analysis of calls from the three groups shows that search-phrase calls are well suited to resolving the velocity of targets, and hence, identifying moving targets in a stationary clutter. Pre-buzz and buzz calls are better suited to resolving range, a feature that may aid the bats in capture of evasive prey after it has been identified.

Search-phase echolocation calls of the New Zealand lesser short-tailed bat (Mystacina tuberculata) and long-tailed bat (Chalinolobus tuberculatus).

This paper describes the search-phase echolocation calls of lesser short-tailed bats (Mystacina tuberculata) and long-tailed bats (Chalinolobus tuberculatus). Calls were recorded from all three subspecies of short-tailed bat and seven populations of long-tailed bat, three in Northland, two in the central North Island, and two in the lower South Island. The calls were recorded in the field and digitised, then three spectral components and one temporal component of the calls were measured. Calls of the lesser short-tailed bat could be loosely classified into subspecies by means of multivariate discriminant function analysis. Similarly, long-tailed bat calls showed regional variation, and discriminant function analysis was able to fit calls to regional groups with a high rate of success. The significance of the results presented is discussed in terms of the conservation of New Zealand bats and the unique ecology of the lesser short-tailed bat.