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Research on the deployment and use of technology to assist learning has seen a significant
rise over the last decades (Aparicio et al., 2017). The focus on course quality, technology,
learning outcome and learner satisfaction in e-learning has led to insufficient attention by
researchers to individual characteristics of learners (Cidral et al., 2017 ; Hsu et al., 2013). The current work aims to bridge this gap by investigating characteristics identified by previous works and backed by theory as influential individual differences in e-learning. These learner characteristics have been suggested as motivational factors (Edmunds et al., 2012) in decisions by learners to interact and exchange information (Luo et al., 2017).
In this work e-learning is defined as interaction dependent information seeking and sharing enabled by technology. This is primarily approached from a media psychology perspective. The role of learner characteristics namely, beliefs about the source of knowledge (Schommer, 1990), learning styles (Felder & Silverman, 1988), need for affect (Maio & Esses, 2001), need for cognition (Cacioppo & Petty, 1982) and power distance (Hofstede, 1980) on interactions to seek and share information in e-learning are investigated. These investigations were shaped by theory and empirical lessons as briefly mentioned in the next paragraphs. Theoretical support for investigations is derived from the technology acceptance model(TAM) by psychologist Davis (1989) and the hyper-personal model by communication scientist Walther (1996). The TAM was used to describe the influence of learner characteristics on decisions to use e-learning systems (Stantchev et al., 2014). The hyper-personal model described why computer-mediated communication thrives in e-learning (Kaye et al., 2016) and how learners interpret messages exchanged online (Hansen et al., 2015). This theoretical framework was followed by empirical reviews which justified the use of interaction and information seeking-sharing as key components of e-learning as well as the selection of learner characteristics. The reviews provided suggestions for the measurement of variables (Kühl et al., 2014) and the investigation design (Dascalau et al., 2015). Investigations were designed and implemented through surveys and quasi experiments which were used for three preliminary studies and two main studies. Samples were selected from Germany and Ghana with same variables tested in both countries. Hypotheses were tested with interaction and information seeking-sharing as dependent variables while beliefs about the source of knowledge, learning styles, need for affect, need for cognition and power distance were independent variables. Firstly, using analyses of variance, the influence of beliefs about the source of knowledge on interaction choices of learners was supported. Secondly, the role of need for cognition on interaction choices of learners was supported by results from a logistic regression. Thirdly, results from multiple linear regressions backed the influence of need for cognition and power distance on information seeking-sharing behaviour of learners. Fourthly, the relationship between need for affect and need for cognition
was supported. The findings may have implications for media psychology research, theories used in this work, research on e-learning, measurement of learner characteristics and the design of e-learning platforms. The findings suggest that, the beliefs learners have about the source of knowledge, their need for cognition and their power distance can influence decisions to interact and seek or share information. The outlook from reviews and findings in this work predicts more research on learner characteristics and a corresponding intensity in the use of e-learning by individuals. It is suggested that future studies investigate the relationship between learner autonomy and power distance. Studies on inter-cultural similarities amongst e-learners in different populations are also
suggested.
Affective states in the context of learning and achievement can influence the learning process essentially. The impact of affective states can be both directly on the learning performance and indirectly mediated via, for example, motivational processes. Positive activating affect is often associated with increased memory skills as well as advantages in creative problem solving. Negative activating affect on the other hand is regarded to impair learning outcomes because of promoting task-irrelevant thinking. While these relationships were found to be relatively stable in correlation studies, causal relationships have been examined rarely so far. This dissertation aims to investigate the effects of positive and negative affective states in multimedia learning settings and to identify potential moderating factors. Therefore, three experimental empirical studies on university students were conducted. In Experiment 1, N = 57 university students were randomly allocated to either a positive or negative affect induction group. Affects were elicited using short film clips. After a 20-minute learning phase in a hypertext-based multimedia learning environment on “functional neuroanatomy” the learners’ knowledge as well as transfer performance were measured. It was assumed that inducing positive activating affect should enhance learning performance. Eliciting negative activating affect on the other hand should impair learning performance. However, it was found that the induction of negative activating affect prior to the learning phase resulted in slight deteriorations in knowledge. Contrary to the assumptions, inducing positive activating affect before the learning phase did not improve learning performance. Experiment 2 induced positive activating affect directly during learning. To induce affective states during the entire duration of the learning phase, Experiment 2 used an emotional design paradigm. Therefore, N = 111 university students were randomly assigned to learn either in an affect inducing multimedia learning environment (use of warm colours and round shapes) or an affectively neutral counterpart (using shades of grey and angular shapes) on the same topic as in Experiment 1. Again, knowledge as well as transfer performance were measured after learning for 20 minutes. In addition, positive and negative affective states were measured before and after learning. Complex interaction patterns between the treatment and initial affective states were found. Specifically, learners with high levels of positive affect before learning showed better transfer performance when they learned in the affect inducing learning environment. Regarding knowledge, those participants who reported high levels of negative activating affect prior to the learning period performed worse. However, the effect on knowledge did not occur for those students learning in the affect inducing learning environment. For knowledge, the treatment therefore protected against poorer performance due to high levels of negative affective states. Results of Experiment 2 showed that the induction of positive activating affect influenced learning performance positively when taking into account affective states prior to the learning phase. In order to confirm these interaction effects, a conceptual replication of the previous experiment was conducted in Experiment 3. Experiment 3 largely retained the former study design, but changed the learning materials and tests used. Analogous to Experiment 2, N = 145 university students learning for 20 minutes in either an affect inducing or an affectively neutral multimedia learning environment on “eukaryotic cell”. To strengthen the treatment, Experiment 3 also used anthropomorphic design elements to induce affective states next to warm colours and round shapes. Moreover, in order to assess the change in affective states more exactly, an additional measurement of positive and negative affective states after half of the learning time was inserted. Knowledge and transfer were assessed again to measure learning performance. The learners’ memory skills were used as an additional learning outcome. To control the influence of potential confounding variables, the participants’ general and current achievement motivation as well as interest, and emotion regulation skills were measured. Contrary to the assumptions, Experiment 3 could not confirm the interaction effects of Experiment 2. Instead, there was a significant impact of positive activating affect prior to the learning phase on transfer, irrespective of the learners’ group affiliation. This effect was further independent of the control variables that were measured. Nevertheless, the results of Experiment 3 fit into the picture of findings regarding “emotional design” in hypermedia learning settings. To date, the few publications that have used this approach propose heterogeneous results, even when using identical materials and procedures.
Are there emotional reactions towards social robots? Could you love a robot? Or, put the other way round: Could you mistreat a robot, tear it apart and sell it? Media reports people honoring military robots with funerals, mourning the “death” of a robotic dog, and granting the humanoid robot Sophia citizenship. But how profound are these reactions? Three experiments take a closer look on emotional reactions towards social robots by investigating the subjective experience of people as well as the motor expressive level. Contexts of varying degrees of Human-Robot Interaction (HRI) sketch a nuanced picture of emotions towards social robots that encompass conscious as well as unconscious reactions. The findings advance the understanding of affective experiences in HRI. It also turns the initial question into: Can emotional reactions towards social robots even be avoided?