Group work and the change of obstacles over time: The influence of learning style and group composition

It is through working in groups that students develop cooperative learning skills and experience. However, group work activity often leads students into a difficult experience, especially for first-year students who are not familiar with group work activities at university. This study explores obstacles faced by first-year students during their group work activities. It investigates whether a group of students with a similar learning style (homogeneous group) experience different obstacles compared to a group of students with a diverse learning style (heterogeneous group). In addition, to identify the difference, if any, between a group formed by a tutor and one where the students form the group themselves, tutor and self-allocated group allocations are explored. This study focuses on obstacles experienced by these students during group work activities. Using a sample of more than 200 students over a period of 3 years, the types and the changes of obstacles in different stages of group life are explored. The findings show that students experience obstacles which can be classified into personal and social, leadership and management, and task-related obstacles. Those obstacles were not static but increased over time. The study also investigates the impact of different methods of forming groups and whether this impacted on obstacles experienced. Overall, different interventions prompted different patterns of obstacle development

GC/MSn analysis of the crude reaction mixtures from Friedel–Crafts acylation: Unambiguous identification and differentiation of 3-aroylbenzofurans from their 4- and 6-regioisomers

Rationale: 3-Aroylbenzofurans and their 2-nitrophenyl derivatives constitute fundamental intermediates for the synthesis of target compounds with pharmaceutical properties. However, their preparation via the Friedel–Crafts acylation of 2-phenylbenzofurans, using Lewis acid as catalyst, often leads to mixtures of regioisomeric aroylbenzofurans that can be challenging to distinguish, thus preventing the reaction characterization. Method: We report a method for the unambiguous identification and differentiation of the desired 3-benzoyl isomers from their 4- and 6-regioisomers in a crude reaction mixture using gas chromatography coupled to multiple-stage mass spectrometric (GC/MSn) analysis performed in collision-induced dissociation (CID) mode. Results: Upon electron ionization, each set of isomers displayed nearly identical mass spectra. MSn revealed fragmentation patterns that varied in the location of the benzoyl group on the benzofuran scaffold: CID experiments performed on the molecular ion allowed the distinction of the 3-acyl isomers from the 4- and 6-regioisomers; CID experiments on the [M − Ar]+ ion allowed the distinction of the 4-benzoyl from the 6-benzoyl regioisomer, when the nitro group is located on the 2-phenyl ring. Moreover, the unusual loss of OH• radical allowed ascertaining the position of the nitro group in 3-acyl regioisomers bearing the NO2 group. The origin of the diagnostic OH• loss was investigated through MSn experiments using 18O-labelled 3-benzoyl derivatives. Conclusions: The method allows the rapid characterization of crude reaction mixtures of benzoylbenzofurans using solely GC/MSn analysis, simplifying the workflow of extensive isolation and purification for structure elucidationThe present work was partially supported by FIR (Fondo Integrativo per la Ricerca – annualità 2018 and 2019), University of CagliariS

Delivering quality along with quantity: the challenge of teaching a large and heterogeneous engineering class

The challenges faced by a lecturer teaching large multidisciplinary engineering classes are identified. These are principally related to the size of the class, the extensive mathematical knowledge that is considered as prerequisite, as well as the heterogeneity of the class due to the diversity of students’ academic background and interests. In order to improve students’ engagement and retention in class, active learning techniques are employed and their impact on the performance of the class is captured through a questionnaire designed for this purpose. The statistics demonstrate that good teaching facilities and a well-prepared lecturer do not suffice for maximising students’ satisfaction, attention and retention. In order to engage the students in a large class setting, it is important to involve them in the lecture process. The employed active learning methods comprising quizzes, in class demonstration and muddiest-point cards induce a remarkably positive impact at almost no additional teaching resources

A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

Audio versus written feedback: exploring learners’ preference and the impact of feedback format on students’ academic performance

Very little is known about the impact of the different types of feedback on students’ academic performance. This paper explores students’ preference in the use of audio and written feedback and how each type of feedback received by students impact on their academic performance in subsequent assignments. The study involved 68 students who were divided into two groups that received either audio or written feedback in their first assignment which was then recalled and applied into the second assignment. An analysis of results obtained in the second assignment was conducted and comparisons made between students in the audio and written feedback group. Students were also surveyed using an online questionnaire to ascertain their perceptions about the type of feedback they had received. The study established that the type of feedback received did not impact on students’ grades in the subsequent assignment. In addition, while students were broadly positive about audio feedback, they indicated a strong preference for written feedback in future assignments. The study recommends, among other things, further investigation into the link between students’ learning styles and their preferences for different types of feedback

Does individual learning styles influence the choice to use a web-based ECG learning programme in a blended learning setting?

<p>Abstract</p> <p>Background</p> <p>The compressed curriculum in modern knowledge-intensive medicine demands useful tools to achieve approved learning aims in a limited space of time. Web-based learning can be used in different ways to enhance learning. Little is however known regarding its optimal utilisation. Our aim was to investigate if the individual learning styles of medical students influence the choice to use a web-based ECG learning programme in a blended learning setting.</p> <p>Methods</p> <p>The programme, with three types of modules (learning content, self-assessment questions and interactive ECG interpretation training), was offered on a voluntary basis during a face to face ECG learning course for undergraduate medical students. The Index of Learning Styles (ILS) and a general questionnaire including questions about computer and Internet usage, preferred future speciality and prior experience of E-learning were used to explore different factors related to the choice of using the programme or not.</p> <p>Results</p> <p>93 (76%) out of 123 students answered the ILS instrument and 91 the general questionnaire. 55 students (59%) were defined as users of the web-based ECG-interpretation programme. Cronbach's alpha was analysed with coefficients above 0.7 in all of the four dimensions of ILS. There were no significant differences with regard to learning styles, as assessed by ILS, between the user and non-user groups; Active/Reflective; Visual/Verbal; Sensing/Intuitive; and Sequential/Global (p = 0.56-0.96). Neither did gender, prior experience of E-learning or preference for future speciality differ between groups.</p> <p>Conclusion</p> <p>Among medical students, neither learning styles according to ILS, nor a number of other characteristics seem to influence the choice to use a web-based ECG programme. This finding was consistent also when the usage of the different modules in the programme were considered. Thus, the findings suggest that web-based learning may attract a broad variety of medical students.</p

ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire

The file attached to this record is the author's final peer reviewed version.E-Learning recommender system effectiveness re- lies upon their ability to recommend appropriate learning con- tents according to the learner learning style and preferences. An effective approach to handle the learner preferences is to build an efficient learner profile in order to gain adaptation and individualisation of the learning environment. It is usually necessary to know learning style and preferences of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learning styles of students in order to adapt the learning process and course content. ULEARN is an adaptive recommender learning system designed to provide learners with personalised learning environment such as course learning objects that match their adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style ques- tionnaire used to initialise the adaptive learner profile. Firstly, the questionnaire is restructured into four groups, one for each learning style dimension; and a study is carried out to determine the order in which questions will be asked in each dimension. Then an algorithm is built upon this ranking of questions to calculate dynamically the initial learning style of the user as they go through the questionnaire

The M3 muscarinic receptor Is required for optimal adaptive immunity to Helminth and bacterial infection

Innate immunity is regulated by cholinergic signalling through nicotinic acetylcholine receptors. We show here that signalling through the M3 muscarinic acetylcholine receptor (M3R) plays an important role in adaptive immunity to both Nippostrongylus brasiliensis and Salmonella enterica serovar Typhimurium, as M3R-/- mice were impaired in their ability to resolve infection with either pathogen. CD4 T cell activation and cytokine production were reduced in M3R-/- mice. Immunity to secondary infection with N. brasiliensis was severely impaired, with reduced cytokine responses in M3R-/- mice accompanied by lower numbers of mucus-producing goblet cells and alternatively activated macrophages in the lungs. Ex vivo lymphocyte stimulation of cells from intact BALB/c mice infected with N. brasiliensis and S. typhimurium with muscarinic agonists resulted in enhanced production of IL-13 and IFN-γ respectively, which was blocked by an M3R-selective antagonist. Our data therefore indicate that cholinergic signalling via the M3R is essential for optimal Th1 and Th2 adaptive immunity to infection

Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user