Aktuelle Anstellung

Wissenschaftliche Mitarbeiterin (Praedoc) an der Freien Universität Berlin, Fachbereich Erziehungswissenschaft und Psychologie, Arbeitsbereich Sonderpädagogik, Schwerpunkt „Entwicklung des Lernens“

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Betreuung von Abschlussarbeiten (Bachelor) zu verschiedenen Themen im Bereich „Entwicklung des Lernens“.

Themenvorschläge für Bachelor- und Masterarbeiten finden Sie unter folgendem Link: 

https://www.ewi-psy.fu-berlin.de/sonderpaedagogik/sonderpaed/studium_lehre/modulabschlussarbeiten/index.html

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Bei freien Kapazitäten betreuen wir Sie gern bei Ihrer Abschlussarbeit.

Doctoral Research Proposal

Besides cognitive processing, affective processing plays a role during learning activities (Moreno & Mayer, 2007; Pekrun & Perry, 2014; Plass & Kaplan, 2016). Affective-cognitive models of learning seek to incorporate the emotional state of learners within the causal chain, producing a learning outcome. Emotion can be defined as a short-term, intense affect caused by a particular object or event (Duffy, Lajoie, Pekrun, & Lachapelle, 2018), such as a learning environment.

Due to the current trend in digitalization, there is growing interest in examining the role of emotions in electronic learning environments (e-learning) and several theories incorporate learning with multimedia, while including the emotional state as a key component, such as Pekrun and Perry (2014)’s Control-Value Theory of Achievement Emotion (CVT), Moreno and Mayer (2007)’s Cognitive-Affective Model of Learning with Media (CATLM) and Plass and Kaplan (2016)’s Integrated Cognitive Affective Model of Learning with Multimedia (ICALM).

In line with these theories, correlational evidence suggests an association between affective processing (e.g., emotions experienced during e-learning) and cognitive processing (e.g., level of en gagement, strategy use) and between affective processing and learning outcomes (e.g., task performance; Stark et al., 2018; Loderer, Pekrun & Lester, 2020) in e-learning environments, while the affective-cognitive pathway explaining these associations can go into two directions: 1) a positive path from learning activity (involving high perceived control and value) to enjoyment, engagement leading to good learning outcomes (Stark et al., 2018; Loderer, Pekrun & Lester, 2020). 2) a negative path from learning episode (involving low perceived control and value) to anxiety, disengagement leading to poor learning outcomes (Stark et al., 2018; Loderer, Pekrun & Lester, 2020).

To better understand these correlational links and infere causality (see Pekrun et al., 2017), further research should focus on 1) investigating influential factors of learner characteristics (e.g., intelligence, attention, memory, emotion recognition), 2) learning processes (e.g., deductive vs. inductive, declarative vs. procedural) and 3) learning settings (e.g., single vs. collaborative learning, digital vs. analogue educational presentation), while considering objective measures of emotional states and learning processes.

In my doctoral thesis, I aim at following these research requests and investigating affective and cognitive processing and their impact on learning outcomes in different learning settings and with differ ent participant groups.

Specifically, as learner‘s emotional states are mostly measured subjectively by self-report surveys (e.g. Achievement Emotion Questionnaire; Pekrun et al., 2011) so far, I want to use software-based tools to analyze emotional facial expressions objectively such as Noldus‘ FaceReader (2022). Further, I aim at co-registering electroencephalography (EEG) to measure how facial emotional expres sions, as a measure of emotional states, are associated with cognitive processing. In the EEG, I will focus on event-related potentials (Schindler & Bublatzky, 2020; Fields, 2023) and frequency bands (Puszta, 2022; Xiao et al., 2025) modulated by emotion, attention and memory, such as late positive potential (LPP; ~300-700 ms, centro-parietal), early posterior negativity (EPN; ~150-300 ms, occip ito-parietal), and N170 (face sensitive; ~130-200 ms, temporo-occipital), fronto-temporal gamma band (30-80 Hz), particularly high gamma sub-band (typically >50 Hz) and frontal theta (4-8 Hz).

As children with learning difficulties, such as dyslexia, were shown to exhibit secondary affective symptoms (Novita, 2016), I will investigate the association between affective and cognitive processing in an e-learning setting (see Roberts, 2021), compared to a typical learning setting, not only in typically developing individuals, but also in children with learning difficulties.