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Tuulikki Alamettälä

dblp:145/3159 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-3247-6324ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
evaluation
0.612022
IRVILAB: Gamified Searching on Multilingual Wikipedia · SIGIR 2022
Computing education › computer science curriculum
information retrieval education
0.212022
IRVILAB: Gamified Searching on Multilingual Wikipedia · SIGIR 2022
Information retrieval › evaluation
relevance judgment
0.212022
IRVILAB: Gamified Searching on Multilingual Wikipedia · SIGIR 2022

Methods — techniques the papers use, named apart from their topics

recall base translation · 1.1gamification · 1.1
YearPublicationVenuePosition
2022 IRVILAB: Gamified Searching on Multilingual Wikipedia
abstract
Information retrieval (IR) evaluation can be considered as a form of competition in matching documents and queries. This paper introduces a learning environment based on gamification of query construction for document retrieval, called IRVILAB (Information Retrieval Virtual Lab). The lab has modules for creating standard evaluation settings, one for topic creation including relevance assessments and another for performance evaluation of user queries. In addition, multilingual Wikipedia online collection enables a module, where relevance assessments are translated to other languages. The underlying game utilizes IR performance metrics to measure and give feedback on participants' information retrieval performance. It aims to improve participants' search skills, subject knowledge and contributes to science education by introducing an experimental method. Distinctive features of the system include algorithmic relevance assessments and automatic recall base translation.
Paavo Arvola, Tuulikki Alamettälä
SIGIR2
2014 Students' group work strategies in source-based writing assignments
abstract
Source‐based writing assignments conducted by groups of students are a common learning task used in information literacy instruction. The fundamental assumption in group assignments is that students' collaboration substantially enhances their learning. The present study focused on the group work strategies adopted by upper secondary school students in source‐based writing assignments. Seventeen groups authored Wikipedia or Wikipedia‐style articles and were interviewed during and after the assignment. Group work strategies were analyzed in 6 activities: planning, searching, assessing sources, reading, writing, and editing. The students used 2 cooperative strategies: delegation and division of work, and 2 collaborative strategies: pair and group collaboration. Division of work into independently conducted parts was the most popular group work strategy. Also group collaboration, where students worked together to complete an activity, was commonly applied. Division of work was justified by efficiency in completing the project and by ease of control in the fair division of contributions. The motivation behind collaboration was related to quality issues and shared responsibility. We suggest that the present designs of learning tasks lead students to avoid collaboration, increasing the risk of low learning outcomes in information literacy instruction.
Eero Sormunen, Mikko Tanni, Tuulikki Alamettälä, Jannica Heinström
J. Assoc. Inf. Sci. Technol.3