Teaching programming to beginners is a complex task. In this paper, the effects of three factors -choice of programming language, problem-solving training and the use of formative assessment -on learning to program were investigated. The study adopted an iterative methodological approach carried out across four consecutive years. To evaluate the effects of each factor (implemented as a single change in each iteration) on students' learning performance, the study used quantitative, objective metrics. The findings revealed that using a syntactically-simple language (Python) instead of a more complex one (Java) facilitated students' learning of programming concepts. Moreover, teaching problem-solving before programming yielded significant improvements in students' performance. These two factors were found to have variable effects on the acquisition of basic programming concepts. Finally, it was observed that effective formative feedback in the context of introductory programming depends on multiple parameters. The paper discusses the implications of these findings, identifies avenues for further research and argues for the importance of studies in computer science education anchored on sound research methodologies to produce generalizable results.
PurposeBusiness intelligence (BI) systems (i.e. technology and procedures that transform raw data into useful information for managers to enable them to make better and faster decisions) have enormous potential to improve organisational efficiency. However, given the high expenditure involved in the deployment of these systems, the factors that will enable their successful integration should be thoroughly considered and assessed before these systems are adopted. Absorptive capacity (ACAP) is the ability of organisations to gather, absorb and strategically influence new external information, and as such, there is a strong theoretical connection between ACAP and BI systems. This research aims to empirically investigate the relationship between the dimensions underpinning ACAP (i.e. acquisition, assimilation, transformation and exploitation) and whether and how they affect the efficiency of BI systems, which, in turn, can enhance organisational efficiency.Design/methodology/approachThis study formulates five hypotheses addressing the effect of ACAP dimensions on BI systems efficiency and the effect of BI systems efficiency on organisational efficiency. It synthesises previous qualitative work and current research to derive sets of measures for each of the key constructs of the study. It follows a quantitative methodology, which involves the collection of survey data from senior managers in the telecommunications industry and the analysis of the data using partial least squares – structural equation modelling (PLS-SEM).FindingsThe results of the analysis confirmed the validity of the constructs and proposed measures and supported all five hypotheses suggesting a strong positive relationship between the ACAP dimensions, acquisition, assimilation, transformation and exploitation and the efficiency of BI systems and a strong effect of BI systems efficiency on organisational efficiency.Practical implicationsThe study offers a comprehensive model of ACAP and BI systems efficiency. The set of measures that underpin these constructs could help researchers understand how ACAP dimensions are practically implemented and could contribute to their efforts to develop ACAP measurement instruments. At the same time, the model can help managers assess the readiness of their firms to adopt BI systems and identify which areas should be further developed, before committing to the substantial financial investment associated with BI systems. It also provides a set of practical solutions that could be implemented to enable a more robust ACAP and support a better integration of BI systems.Originality/valueFollowing an empirical approach, this study refines one’s theoretical and practical understanding of ACAP as an organisational dynamic capability and its dimensions; it provides an account on how each dimension affects different aspects of BI systems efficiency, which, in turn, may contribute to the improvement of organisational efficiency. Moreover, the study reframes ACAP measures as a set of requirements that can be practically assessed and followed before attempting to purchase BI systems.
Despite the prevalence of mental health conditions, stigma, lack of awareness, and limited resources impede access to care, creating a need to improve mental health support. The recent surge in scientific and commercial interest in conversational agents and their potential to improve diagnosis and treatment seems a potentially fruitful area in this respect, particularly for young adults who widely use such systems in other contexts. Yet, there is little research that considers the acceptability of conversational agents in mental health. This study, therefore, presents three research activities that explore whether conversational agents and, in particular, chatbots can be an acceptable solution in mental healthcare for young adults. First, a survey of young adults (in a university setting) provides an understanding of the landscape of mental health in this age group and of their views around mental health technology, including chatbots. Second, a literature review synthesises current evidence relating to the acceptability of mental health conversational agents and points to future research priorities. Third, interviews with counsellors who work with young adults, supported by a chatbot prototype and user-centred design techniques, reveal the perceived benefits and potential roles of mental health chatbots from the perspective of mental health professionals, while suggesting preconditions for the acceptability of the technology. Taken together, these research activities: provide evidence that chatbots are an acceptable solution to offering mental health support for young adults; identify specific challenges relating to both the technology and environment; and argue for the application of user-centred approaches during development of mental health chatbots and more systematic and rigorous evaluations of the resulting solutions.
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