Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Barbara Means's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Barbara Means with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Barbara Means more than expected).
This network shows the impact of papers produced by Barbara Means. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Barbara Means. The network helps show where Barbara Means may publish in the future.
Co-authorship network of co-authors of Barbara Means
This figure shows the co-authorship network connecting the top 25 collaborators of Barbara Means.
A scholar is included among the top collaborators of Barbara Means based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Barbara Means. Barbara Means is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gallagher, Lawrence P., Barbara Means, & Christine Padilla. (2008). Teachers' Use of Student Data Systems to Improve Instruction: 2005 to 2007..20 indexed citations
8.
Dynarski, Mark, Roberto Agodini, Sheila Heaviside, et al.. (2007). Effectiveness of Reading and Mathematics Software Products: Findings from the First Student Cohort. Report to Congress..71 indexed citations
9.
Means, Barbara, Lawrence P. Gallagher, & Christine Padilla. (2007). Teachers' Use of Student Data Systems to Improve Instruction..39 indexed citations
10.
Haertel, Geneva D. & Barbara Means. (2003). Evaluating educational technology : effective research designs for improving learning. Teachers College Press eBooks.38 indexed citations
11.
Means, Barbara. (2002). Evaluating Learning Technologies: Comments on "A Framework for Quality in Educational Technology Programs" by Confrey, Sabelli, and Sheingold.. Educational Technology archive. 42(3). 37–40.1 indexed citations
12.
Means, Barbara, et al.. (2002). Increasing Student Learning Through Multimedia Projects.29 indexed citations
13.
Means, Barbara. (2001). Technology Use in Tomorrow's Schools.. Educational leadership. 58(4). 57–61.12 indexed citations
14.
Penuel, William R., et al.. (2000). The Multimedia Challenge. Educational leadership. 58(2). 34–38.9 indexed citations
Means, Barbara, et al.. (1995). Beyond the Classroom: Restructuring Schools with Technology. Phi Delta Kappan. 77(1). 69–72.17 indexed citations
17.
Means, Barbara, et al.. (1995). Technology's Role within Constructivist Classrooms. 1995(1).20 indexed citations
18.
Means, Barbara, et al.. (1994). The Link between Technology and Authentic Learning.. Educational leadership. 51(7). 15–18.75 indexed citations
19.
Means, Barbara & Michael S. Knapp. (1991). Cognitive Approaches to Teaching Advanced Skills to Educationally Disadvantaged Students.. Phi Delta Kappan. 73(4).53 indexed citations
20.
Means, Barbara & Janice H. Laurence. (1984). Characteristics and Performance of Recruits Enlisted with General Education Development (GED) Credentials. Defense Technical Information Center (DTIC).3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.