Juliana M A L Andres
Biographic Data
| ID | 4601798 |
|---|---|
| NAME | Juliana M A L Andres |
| GIVEN NAMES | Juliana M A L |
| FAMILY NAME | Andres |
| SIGNATURE | ANDRES J M A L |
| AFFILIATIONS | University of Central Florida |
| ORCID | 0000-0002-7599-6768 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
A balance between stability and flexibility
To ensure learning efficiency in game‐based learning (GBL), learners must regulate cognitive, affective, metacognitive and motivational (CAMM) processes, collectively known as self‐regulated learning (SRL). SRL is dynamic and non‐linear, characterized by regulatory patterns and CAMM interactions that lead to macro‐level SRL behaviours. In this paper, we explore SRL as a complex system by analysing patterns and interactions among CAMM processes du…
Novice and expert self-regulated learning phase transitions in medical diagnosis
Expertise plays a significant role in shaping self-regulated learning (SRL) by influencing how individuals set goals, monitor progress, employ strategies, and reflect on their learning process. However, comprehensive data on this link is sparse in medical contexts. This paper investigates the transitions of SRL phases during clinical-reasoning tasks with a multimedia system, CresME, designed to elicit clinical-reasoning processes using illness sc…
The Confrustion Constellation
There has been considerable research on confusion and frustration that has treated them as two unitary constructs, distinct from each other. In this article, we argue that there is instead a constellation of different types of confusion and frustration, with different antecedents, manifestations, and impacts, and that the commonalities between many types of confusion and frustration justify thinking of them as part of the same constellation of af…
System design for using multimodal trace data in modeling self-regulated learning
Self-regulated learning (SRL) integrates monitoring and controlling of cognitive, affective, metacognitive, and motivational processes during learning in pursuit of goals. Researchers have begun using multimodal data (e.g., concurrent verbalizations, eye movements, on-line behavioral traces, facial expressions, screen recordings of learner-system interactions, and physiological sensors) to investigate triggers and temporal dynamics of SRL and how…
Emotions and the Comprehension of Single versus Multiple Texts during Game-based Learning
This study examined 57 learners’ emotions (i.e., joy, anger, confusion, frustration) as they engaged with scientific content while learning about microbiology with Crystal Island, a game-based learning environment (GBLE). Measures of learners’ prior knowledge, in-game text comprehension, facial expressions of emotion, and posttest reading comprehension were collected to examine the relationship between emotions and single- and multiple-text compr…
Quantifying Scientific Thinking Using Multichannel Data With Crystal Island
Individualizing learning by quantifying scientific thinking using multichannel data during game-based learning remains a significant challenge for researchers and educators. Not only do empirical studies find that learners do not possess sufficient scientific-thinking skills to deal with the demands of the 21st century, but there is little agreement in how researchers should accurately and dynamically capture scientific thinking with game-based l…
Multimodal learning analytics for game‐based learning
A distinctive feature of game‐based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor‐based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students’ interactions with game‐based learning envi…
Emotions and the Comprehension of Single versus Multiple Texts during Game-based Learning
This study examined 57 learners’ emotions (i.e., joy, anger, confusion, frustration) as they engaged with scientific content while learning about microbiology with Crystal Island, a game-based learning environment (GBLE). Measures of learners’ prior knowledge, in-game text comprehension, facial expressions of emotion, and posttest reading comprehension were collected to examine the relationship between emotions and single- and multiple-text compr…
Quantifying Scientific Thinking Using Multichannel Data With Crystal Island
Individualizing learning by quantifying scientific thinking using multichannel data during game-based learning remains a significant challenge for researchers and educators. Not only do empirical studies find that learners do not possess sufficient scientific-thinking skills to deal with the demands of the 21st century, but there is little agreement in how researchers should accurately and dynamically capture scientific thinking with game-based l…
Multimodal learning analytics for game‐based learning
A distinctive feature of game‐based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor‐based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students’ interactions with game‐based learning envi…
System design for using multimodal trace data in modeling self-regulated learning
Self-regulated learning (SRL) integrates monitoring and controlling of cognitive, affective, metacognitive, and motivational processes during learning in pursuit of goals. Researchers have begun using multimodal data (e.g., concurrent verbalizations, eye movements, on-line behavioral traces, facial expressions, screen recordings of learner-system interactions, and physiological sensors) to investigate triggers and temporal dynamics of SRL and how…
Emotions and the Comprehension of Single versus Multiple Texts during Game-based Learning
This study examined 57 learners’ emotions (i.e., joy, anger, confusion, frustration) as they engaged with scientific content while learning about microbiology with Crystal Island, a game-based learning environment (GBLE). Measures of learners’ prior knowledge, in-game text comprehension, facial expressions of emotion, and posttest reading comprehension were collected to examine the relationship between emotions and single- and multiple-text compr…
Novice and expert self-regulated learning phase transitions in medical diagnosis
Expertise plays a significant role in shaping self-regulated learning (SRL) by influencing how individuals set goals, monitor progress, employ strategies, and reflect on their learning process. However, comprehensive data on this link is sparse in medical contexts. This paper investigates the transitions of SRL phases during clinical-reasoning tasks with a multimedia system, CresME, designed to elicit clinical-reasoning processes using illness sc…
The Confrustion Constellation
There has been considerable research on confusion and frustration that has treated them as two unitary constructs, distinct from each other. In this article, we argue that there is instead a constellation of different types of confusion and frustration, with different antecedents, manifestations, and impacts, and that the commonalities between many types of confusion and frustration justify thinking of them as part of the same constellation of af…
A balance between stability and flexibility
To ensure learning efficiency in game‐based learning (GBL), learners must regulate cognitive, affective, metacognitive and motivational (CAMM) processes, collectively known as self‐regulated learning (SRL). SRL is dynamic and non‐linear, characterized by regulatory patterns and CAMM interactions that lead to macro‐level SRL behaviours. In this paper, we explore SRL as a complex system by analysing patterns and interactions among CAMM processes du…
Computer Science (5 works) · Innovative Teaching and Learning Methods (5 works) · Psychology (5 works) · Educational Games and Gamification (4 works) · Artificial Intelligence (3 works) · Data science (3 works) · Analytics (2 works) · Artificial Intelligence (2 works) · Cognition (2 works) · Cognitive psychology (2 works)