Understanding the "Pathway" Towards a Searcher's Learning Objective
August 15, 2022 Β· Declared Dead Β· π ACM Trans. Inf. Syst.
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kelsey Urgo, Jaime Arguello
arXiv ID
2208.07275
Category
cs.IR: Information Retrieval
Citations
14
Venue
ACM Trans. Inf. Syst.
Last Checked
4 months ago
Abstract
Search systems are often used to support learning-oriented goals. This trend has given rise to the "search-as-learning" movement, which proposes that search systems should be designed to support learning. To this end, an important research question is: How does a searcher's \emph{type} of learning objective influence their trajectory (or \emph{pathway}) towards that objective? We report on a lab study ($N=36$) in which participants gathered information to meet a specific type of learning objective. To characterize learning objectives \emph{and pathways}, we leveraged Anderson and Krathwohl's (A\&K's) taxonomy \cite{anderson2001taxonomy}. Participants completed learning-oriented search tasks that varied along three cognitive processes (apply, evaluate, create) and three knowledge types (factual, conceptual, procedural knowledge). A \emph{pathway} is defined as a sequence of \emph{learning instances} (e.g., subgoals) that were also each classified into cells from A\&K's taxonomy. Our study used a think-aloud protocol, and pathways were generated through a qualitative analysis of participants' think-aloud comments and recorded screen activities. We investigate three research questions. First, in RQ1, we study the impact of the learning objective on pathway characteristics (e.g., pathway length). Second, in RQ2, we study the impact of the learning objective on the types of A\&K cells traversed along the pathway. Third, in RQ3, we study common and uncommon \emph{transitions} between A\&K cells along pathways conditioned on the knowledge type of the objective. We discuss implications of our results for designing search systems to support learning.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
R.I.P.
π»
Ghosted
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted